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+[[package]]
+name = "web-sys"
+version = "0.3.61"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e33b99f4b23ba3eec1a53ac264e35a755f00e966e0065077d6027c0f575b0b97"
+dependencies = [
+ "js-sys",
+ "wasm-bindgen",
+]
+
+[[package]]
+name = "which"
+version = "4.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2441c784c52b289a054b7201fc93253e288f094e2f4be9058343127c4226a269"
+dependencies = [
+ "either",
+ "libc",
+ "once_cell",
+]
+
+[[package]]
+name = "winapi"
+version = "0.3.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5c839a674fcd7a98952e593242ea400abe93992746761e38641405d28b00f419"
+dependencies = [
+ "winapi-i686-pc-windows-gnu",
+ "winapi-x86_64-pc-windows-gnu",
+]
+
+[[package]]
+name = "winapi-i686-pc-windows-gnu"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6"
+
+[[package]]
+name = "winapi-util"
+version = "0.1.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "70ec6ce85bb158151cae5e5c87f95a8e97d2c0c4b001223f33a334e3ce5de178"
+dependencies = [
+ "winapi",
+]
+
+[[package]]
+name = "winapi-x86_64-pc-windows-gnu"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f"
+
+[[package]]
+name = "windows-sys"
+version = "0.42.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5a3e1820f08b8513f676f7ab6c1f99ff312fb97b553d30ff4dd86f9f15728aa7"
+dependencies = [
+ "windows_aarch64_gnullvm 0.42.2",
+ "windows_aarch64_msvc 0.42.2",
+ "windows_i686_gnu 0.42.2",
+ "windows_i686_msvc 0.42.2",
+ "windows_x86_64_gnu 0.42.2",
+ "windows_x86_64_gnullvm 0.42.2",
+ "windows_x86_64_msvc 0.42.2",
+]
+
+[[package]]
+name = "windows-sys"
+version = "0.45.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "75283be5efb2831d37ea142365f009c02ec203cd29a3ebecbc093d52315b66d0"
+dependencies = [
+ "windows-targets 0.42.2",
+]
+
+[[package]]
+name = "windows-sys"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "677d2418bec65e3338edb076e806bc1ec15693c5d0104683f2efe857f61056a9"
+dependencies = [
+ "windows-targets 0.48.0",
+]
+
+[[package]]
+name = "windows-targets"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8e5180c00cd44c9b1c88adb3693291f1cd93605ded80c250a75d472756b4d071"
+dependencies = [
+ "windows_aarch64_gnullvm 0.42.2",
+ "windows_aarch64_msvc 0.42.2",
+ "windows_i686_gnu 0.42.2",
+ "windows_i686_msvc 0.42.2",
+ "windows_x86_64_gnu 0.42.2",
+ "windows_x86_64_gnullvm 0.42.2",
+ "windows_x86_64_msvc 0.42.2",
+]
+
+[[package]]
+name = "windows-targets"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7b1eb6f0cd7c80c79759c929114ef071b87354ce476d9d94271031c0497adfd5"
+dependencies = [
+ "windows_aarch64_gnullvm 0.48.0",
+ "windows_aarch64_msvc 0.48.0",
+ "windows_i686_gnu 0.48.0",
+ "windows_i686_msvc 0.48.0",
+ "windows_x86_64_gnu 0.48.0",
+ "windows_x86_64_gnullvm 0.48.0",
+ "windows_x86_64_msvc 0.48.0",
+]
+
+[[package]]
+name = "windows_aarch64_gnullvm"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "597a5118570b68bc08d8d59125332c54f1ba9d9adeedeef5b99b02ba2b0698f8"
+
+[[package]]
+name = "windows_aarch64_gnullvm"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "91ae572e1b79dba883e0d315474df7305d12f569b400fcf90581b06062f7e1bc"
+
+[[package]]
+name = "windows_aarch64_msvc"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e08e8864a60f06ef0d0ff4ba04124db8b0fb3be5776a5cd47641e942e58c4d43"
+
+[[package]]
+name = "windows_aarch64_msvc"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b2ef27e0d7bdfcfc7b868b317c1d32c641a6fe4629c171b8928c7b08d98d7cf3"
+
+[[package]]
+name = "windows_i686_gnu"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c61d927d8da41da96a81f029489353e68739737d3beca43145c8afec9a31a84f"
+
+[[package]]
+name = "windows_i686_gnu"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "622a1962a7db830d6fd0a69683c80a18fda201879f0f447f065a3b7467daa241"
+
+[[package]]
+name = "windows_i686_msvc"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "44d840b6ec649f480a41c8d80f9c65108b92d89345dd94027bfe06ac444d1060"
+
+[[package]]
+name = "windows_i686_msvc"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4542c6e364ce21bf45d69fdd2a8e455fa38d316158cfd43b3ac1c5b1b19f8e00"
+
+[[package]]
+name = "windows_x86_64_gnu"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8de912b8b8feb55c064867cf047dda097f92d51efad5b491dfb98f6bbb70cb36"
+
+[[package]]
+name = "windows_x86_64_gnu"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ca2b8a661f7628cbd23440e50b05d705db3686f894fc9580820623656af974b1"
+
+[[package]]
+name = "windows_x86_64_gnullvm"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "26d41b46a36d453748aedef1486d5c7a85db22e56aff34643984ea85514e94a3"
+
+[[package]]
+name = "windows_x86_64_gnullvm"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7896dbc1f41e08872e9d5e8f8baa8fdd2677f29468c4e156210174edc7f7b953"
+
+[[package]]
+name = "windows_x86_64_msvc"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9aec5da331524158c6d1a4ac0ab1541149c0b9505fde06423b02f5ef0106b9f0"
+
+[[package]]
+name = "windows_x86_64_msvc"
+version = "0.48.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1a515f5799fe4961cb532f983ce2b23082366b898e52ffbce459c86f67c8378a"
+
+[[package]]
+name = "winreg"
+version = "0.10.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "80d0f4e272c85def139476380b12f9ac60926689dd2e01d4923222f40580869d"
+dependencies = [
+ "winapi",
+]
+
+[[package]]
+name = "xattr"
+version = "0.2.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6d1526bbe5aaeb5eb06885f4d987bcdfa5e23187055de9b83fe00156a821fabc"
+dependencies = [
+ "libc",
+]
+
+[[package]]
+name = "zip"
+version = "0.6.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0445d0fbc924bb93539b4316c11afb121ea39296f99a3c4c9edad09e3658cdef"
+dependencies = [
+ "aes",
+ "byteorder",
+ "bzip2",
+ "constant_time_eq",
+ "crc32fast",
+ "crossbeam-utils",
+ "flate2",
+ "hmac",
+ "pbkdf2",
+ "sha1",
+ "time",
+ "zstd",
+]
+
+[[package]]
+name = "zstd"
+version = "0.11.2+zstd.1.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "20cc960326ece64f010d2d2107537f26dc589a6573a316bd5b1dba685fa5fde4"
+dependencies = [
+ "zstd-safe",
+]
+
+[[package]]
+name = "zstd-safe"
+version = "5.0.2+zstd.1.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1d2a5585e04f9eea4b2a3d1eca508c4dee9592a89ef6f450c11719da0726f4db"
+dependencies = [
+ "libc",
+ "zstd-sys",
+]
+
+[[package]]
+name = "zstd-sys"
+version = "2.0.8+zstd.1.5.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5556e6ee25d32df2586c098bbfa278803692a20d0ab9565e049480d52707ec8c"
+dependencies = [
+ "cc",
+ "libc",
+ "pkg-config",
+]
diff --git a/Cargo.toml b/Cargo.toml
new file mode 100644
index 0000000000000000000000000000000000000000..af479c04bbc6e9f0784c278184e2d8b16e96bea8
--- /dev/null
+++ b/Cargo.toml
@@ -0,0 +1,16 @@
+[workspace]
+members = [
+    "router",
+    "router/client",
+    "router/grpc-metadata",
+    "launcher"
+]
+exclude = [
+    "benchmark"
+]
+
+[profile.release]
+debug = 1
+incremental = true
+lto = "off"
+panic = "abort"
diff --git a/Dockerfile b/Dockerfile
index f93d1a3b2738f0cea3af549894cfa10fb45a5c07..d59acc108befe152b03d5e3376e60941ca29bd69 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,99 +1,179 @@
-FROM nvidia/cuda:11.3.1-base-ubuntu20.04
-
-ENV DEBIAN_FRONTEND=noninteractive \
-	TZ=Europe/Paris
-
-# Remove any third-party apt sources to avoid issues with expiring keys.
-# Install some basic utilities
-RUN rm -f /etc/apt/sources.list.d/*.list && \
-    apt-get update && apt-get install -y --no-install-recommends \
-    curl \
-    ca-certificates \
-    sudo \
-    git \
-    git-lfs \
-    zip \
-    unzip \
-    htop \
-    bzip2 \
-    libx11-6 \
-    build-essential \
-    libsndfile-dev \
-    software-properties-common \
- && rm -rf /var/lib/apt/lists/*
-
-RUN add-apt-repository ppa:flexiondotorg/nvtop && \
-    apt-get upgrade -y && \
-    apt-get install -y --no-install-recommends nvtop
-
-RUN curl -sL https://deb.nodesource.com/setup_14.x  | bash - && \
-    apt-get install -y nodejs && \
-    npm install -g configurable-http-proxy
-
-# Create a working directory
-WORKDIR /app
-
-# Create a non-root user and switch to it
-RUN adduser --disabled-password --gecos '' --shell /bin/bash user \
- && chown -R user:user /app
-RUN echo "user ALL=(ALL) NOPASSWD:ALL" > /etc/sudoers.d/90-user
-USER user
-
-# All users can use /home/user as their home directory
-ENV HOME=/home/user
-RUN mkdir $HOME/.cache $HOME/.config \
- && chmod -R 777 $HOME
-
-# Set up the Conda environment
-ENV CONDA_AUTO_UPDATE_CONDA=false \
-    PATH=$HOME/miniconda/bin:$PATH
-RUN curl -sLo ~/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-py39_4.10.3-Linux-x86_64.sh \
- && chmod +x ~/miniconda.sh \
- && ~/miniconda.sh -b -p ~/miniconda \
- && rm ~/miniconda.sh \
- && conda clean -ya
-
-WORKDIR $HOME/app
-
-#######################################
-# Start root user section
-#######################################
-
-USER root
-
-# User Debian packages
-## Security warning : Potential user code executed as root (build time)
-RUN --mount=target=/root/packages.txt,source=packages.txt \
-    apt-get update && \
-    xargs -r -a /root/packages.txt apt-get install -y --no-install-recommends \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN --mount=target=/root/on_startup.sh,source=on_startup.sh,readwrite \
-	bash /root/on_startup.sh
-
-#######################################
-# End root user section
-#######################################
-
-USER user
-
-# Python packages
-RUN --mount=target=requirements.txt,source=requirements.txt \
-    pip install --no-cache-dir --upgrade -r requirements.txt
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-RUN chmod +x start_server.sh
-
-COPY --chown=user login.html /home/user/miniconda/lib/python3.9/site-packages/jupyter_server/templates/login.html
-
-ENV PYTHONUNBUFFERED=1 \
-	GRADIO_ALLOW_FLAGGING=never \
-	GRADIO_NUM_PORTS=1 \
-	GRADIO_SERVER_NAME=0.0.0.0 \
-	GRADIO_THEME=huggingface \
-	SYSTEM=spaces \
-	SHELL=/bin/bash
-
-CMD ["./start_server.sh"]
+# Rust builder
+FROM lukemathwalker/cargo-chef:latest-rust-1.69 AS chef
+WORKDIR /usr/src
+
+FROM chef as planner
+COPY Cargo.toml Cargo.toml
+COPY rust-toolchain.toml rust-toolchain.toml
+COPY proto proto
+COPY router router
+COPY launcher launcher
+RUN cargo chef prepare --recipe-path recipe.json
+
+FROM chef AS builder
+
+ARG GIT_SHA
+ARG DOCKER_LABEL
+
+RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
+    curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \
+    unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \
+    unzip -o $PROTOC_ZIP -d /usr/local 'include/*' && \
+    rm -f $PROTOC_ZIP
+
+COPY --from=planner /usr/src/recipe.json recipe.json
+RUN cargo chef cook --release --recipe-path recipe.json
+
+COPY Cargo.toml Cargo.toml
+COPY rust-toolchain.toml rust-toolchain.toml
+COPY proto proto
+COPY router router
+COPY launcher launcher
+RUN cargo build --release
+
+# Python builder
+# Adapted from: https://github.com/pytorch/pytorch/blob/master/Dockerfile
+FROM debian:bullseye-slim as pytorch-install
+
+ARG PYTORCH_VERSION=2.0.0
+ARG PYTHON_VERSION=3.9
+ARG CUDA_VERSION=11.8
+ARG MAMBA_VERSION=23.1.0-1
+ARG CUDA_CHANNEL=nvidia
+ARG INSTALL_CHANNEL=pytorch
+# Automatically set by buildx
+ARG TARGETPLATFORM
+
+ENV PATH /opt/conda/bin:$PATH
+
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+        build-essential \
+        ca-certificates \
+        ccache \
+        curl \
+        git && \
+        rm -rf /var/lib/apt/lists/*
+
+# Install conda
+# translating Docker's TARGETPLATFORM into mamba arches
+RUN case ${TARGETPLATFORM} in \
+         "linux/arm64")  MAMBA_ARCH=aarch64  ;; \
+         *)              MAMBA_ARCH=x86_64   ;; \
+    esac && \
+    curl -fsSL -v -o ~/mambaforge.sh -O  "https://github.com/conda-forge/miniforge/releases/download/${MAMBA_VERSION}/Mambaforge-${MAMBA_VERSION}-Linux-${MAMBA_ARCH}.sh"
+RUN chmod +x ~/mambaforge.sh && \
+    bash ~/mambaforge.sh -b -p /opt/conda && \
+    rm ~/mambaforge.sh
+
+# Install pytorch
+# On arm64 we exit with an error code
+RUN case ${TARGETPLATFORM} in \
+         "linux/arm64")  exit 1 ;; \
+         *)              /opt/conda/bin/conda update -y conda &&  \
+                         /opt/conda/bin/conda install -c "${INSTALL_CHANNEL}" -c "${CUDA_CHANNEL}" -y "python=${PYTHON_VERSION}" pytorch==$PYTORCH_VERSION "pytorch-cuda=$(echo $CUDA_VERSION | cut -d'.' -f 1-2)"  ;; \
+    esac && \
+    /opt/conda/bin/conda clean -ya
+
+# CUDA kernels builder image
+FROM pytorch-install as kernel-builder
+
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+        ninja-build \
+        && rm -rf /var/lib/apt/lists/*
+
+RUN /opt/conda/bin/conda install -c "nvidia/label/cuda-11.8.0"  cuda==11.8 && \
+    /opt/conda/bin/conda clean -ya
+
+
+# Build Flash Attention CUDA kernels
+FROM kernel-builder as flash-att-builder
+
+WORKDIR /usr/src
+
+COPY server/Makefile-flash-att Makefile
+
+# Build specific version of flash attention
+RUN make build-flash-attention
+
+# Build Transformers CUDA kernels
+FROM kernel-builder as transformers-builder
+
+WORKDIR /usr/src
+
+COPY server/Makefile-transformers Makefile
+
+# Build specific version of transformers
+RUN BUILD_EXTENSIONS="True" make build-transformers
+
+# Text Generation Inference base image
+FROM debian:bullseye-slim as base
+
+# Conda env
+ENV PATH=/opt/conda/bin:$PATH \
+    CONDA_PREFIX=/opt/conda
+
+# Text Generation Inference base env
+ENV HUGGINGFACE_HUB_CACHE=/data \
+    HF_HUB_ENABLE_HF_TRANSFER=1 \
+    MODEL_ID=bigscience/bloom-560m \
+    QUANTIZE=false \
+    NUM_SHARD=1 \
+    PORT=80
+
+LABEL com.nvidia.volumes.needed="nvidia_driver"
+
+WORKDIR /usr/src
+
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+        libssl-dev \
+        ca-certificates \
+        make \
+        && rm -rf /var/lib/apt/lists/*
+
+# Copy conda with PyTorch installed
+COPY --from=pytorch-install /opt/conda /opt/conda
+
+# Copy build artifacts from flash attention builder
+COPY --from=flash-att-builder /usr/src/flash-attention/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
+COPY --from=flash-att-builder /usr/src/flash-attention/csrc/layer_norm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
+COPY --from=flash-att-builder /usr/src/flash-attention/csrc/rotary/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
+
+# Copy build artifacts from transformers builder
+COPY --from=transformers-builder /usr/src/transformers /usr/src/transformers
+COPY --from=transformers-builder /usr/src/transformers/build/lib.linux-x86_64-cpython-39/transformers /usr/src/transformers/src/transformers
+
+# Install transformers dependencies
+RUN cd /usr/src/transformers && pip install -e . --no-cache-dir && pip install einops --no-cache-dir
+
+# Install server
+COPY proto proto
+COPY server server
+COPY server/Makefile server/Makefile
+RUN cd server && \
+    make gen-server && \
+    pip install -r requirements.txt && \
+    pip install ".[bnb, accelerate]" --no-cache-dir
+
+# Install router
+COPY --from=builder /usr/src/target/release/text-generation-router /usr/local/bin/text-generation-router
+# Install launcher
+COPY --from=builder /usr/src/target/release/text-generation-launcher /usr/local/bin/text-generation-launcher
+
+# AWS Sagemaker compatbile image
+FROM base as sagemaker
+
+COPY sagemaker-entrypoint.sh entrypoint.sh
+RUN chmod +x entrypoint.sh
+
+# NVIDIA env vars
+ENV NVIDIA_VISIBLE_DEVICES all
+ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
+ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
+
+ENTRYPOINT ["./entrypoint.sh"]
+
+# Final image
+FROM base
+
+ENTRYPOINT ["text-generation-launcher"]
+CMD ["--json-output"]
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..7d0e80345c787d5080678c89b3834b50e399c3f5
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
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+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
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+
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+
+      "Work" shall mean the work of authorship, whether in Source or
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
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+      the terms of any separate license agreement you may have executed
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+   6. Trademarks. This License does not grant permission to use the trade
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+   7. Disclaimer of Warranty. Unless required by applicable law or
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+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
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diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..032a49de44d806ff459698a1662e66eab23412c1
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,45 @@
+install-server:
+	cd server && make install
+
+install-router:
+	cd router && cargo install --path .
+
+install-launcher:
+	cd launcher && cargo install --path .
+
+install-benchmark:
+	cd benchmark && cargo install --path .
+
+install: install-server install-router install-launcher
+
+server-dev:
+	cd server && make run-dev
+
+router-dev:
+	cd router && cargo run -- --port 8080
+
+integration-tests: install-router install-launcher
+	cargo test
+
+python-server-tests:
+	HF_HUB_ENABLE_HF_TRANSFER=1 pytest server/tests
+
+python-client-tests:
+	pytest clients/python/tests
+
+python-tests: python-server-tests python-client-tests
+
+run-bloom-560m:
+	text-generation-launcher --model-id bigscience/bloom-560m --num-shard 2 --port 8080
+
+run-bloom-560m-quantize:
+	text-generation-launcher --model-id bigscience/bloom-560m --num-shard 2 --quantize --port 8080
+
+download-bloom:
+	HF_HUB_ENABLE_HF_TRANSFER=1 text-generation-server download-weights bigscience/bloom
+
+run-bloom:
+	text-generation-launcher --model-id bigscience/bloom --num-shard 8 --port 8080
+
+run-bloom-quantize:
+	text-generation-launcher --model-id bigscience/bloom --num-shard 8 --quantize --port 8080
\ No newline at end of file
diff --git a/aml/deployment.yaml b/aml/deployment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..320eba24a92000f74fecbd3f9bc037b44d14e4dc
--- /dev/null
+++ b/aml/deployment.yaml
@@ -0,0 +1,38 @@
+$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
+name: bloom-deployment
+endpoint_name: bloom-inference
+model: azureml:bloom-safetensors:1
+model_mount_path: /var/azureml-model
+environment_variables:
+  WEIGHTS_CACHE_OVERRIDE: /var/azureml-model/bloom-safetensors
+  MODEL_ID: bigscience/bloom
+  NUM_SHARD: 8
+environment:
+  image: db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation-inference:0.2.0
+  inference_config:
+    liveness_route:
+      port: 80
+      path: /health
+    readiness_route:
+      port: 80
+      path: /health
+    scoring_route:
+      port: 80
+      path: /generate
+instance_type: Standard_ND96amsr_A100_v4
+request_settings:
+  request_timeout_ms: 90000
+  max_concurrent_requests_per_instance: 256
+liveness_probe:
+  initial_delay: 600
+  timeout: 90
+  period: 120
+  success_threshold: 1
+  failure_threshold: 5
+readiness_probe:
+  initial_delay: 600
+  timeout: 90
+  period: 120
+  success_threshold: 1
+  failure_threshold: 5
+instance_count: 1
diff --git a/aml/endpoint.yaml b/aml/endpoint.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f2f01d5e1c3f94ee76a5c44acb13b31ddf5059af
--- /dev/null
+++ b/aml/endpoint.yaml
@@ -0,0 +1,3 @@
+$schema: https://azuremlsdk2.blob.core.windows.net/latest/managedOnlineEndpoint.schema.json
+name: bloom-inference
+auth_mode: key
diff --git a/aml/model.yaml b/aml/model.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bfcdd33fedebbd943a20b5cd598c1a28f486dfad
--- /dev/null
+++ b/aml/model.yaml
@@ -0,0 +1,3 @@
+$schema: https://azuremlschemas.azureedge.net/latest/model.schema.json
+name: bloom-safetensors
+path: /data/bloom-safetensors
diff --git a/assets/architecture.jpg b/assets/architecture.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c4a511c9db7a690b991e95e36aab4e1a5b5fef57
Binary files /dev/null and b/assets/architecture.jpg differ
diff --git a/assets/benchmark.png b/assets/benchmark.png
new file mode 100644
index 0000000000000000000000000000000000000000..64d538a0598d57102b82f5984d2437f9db129da2
Binary files /dev/null and b/assets/benchmark.png differ
diff --git a/benchmark/Cargo.lock b/benchmark/Cargo.lock
new file mode 100644
index 0000000000000000000000000000000000000000..81c1180e307aad38ecf4f1d4f0e063949f9be230
--- /dev/null
+++ b/benchmark/Cargo.lock
@@ -0,0 +1,2884 @@
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+ "winapi",
+]
+
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+ "aes",
+ "byteorder",
+ "bzip2",
+ "constant_time_eq",
+ "crc32fast",
+ "crossbeam-utils",
+ "flate2",
+ "hmac",
+ "pbkdf2",
+ "sha1",
+ "time",
+ "zstd",
+]
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+ "zstd-safe",
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+ "libc",
+ "zstd-sys",
+]
+
+[[package]]
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+dependencies = [
+ "cc",
+ "libc",
+ "pkg-config",
+]
diff --git a/benchmark/Cargo.toml b/benchmark/Cargo.toml
new file mode 100644
index 0000000000000000000000000000000000000000..4a6651c84882112520db5811d501df97cb98b5e9
--- /dev/null
+++ b/benchmark/Cargo.toml
@@ -0,0 +1,35 @@
+[package]
+name = "text-generation-benchmark"
+version = "0.1.0"
+edition = "2021"
+authors = ["Olivier Dehaene"]
+description = "Text Generation Benchmarking tool"
+
+[profile.release]
+debug = 1
+incremental = true
+lto = "off"
+panic = "abort"
+
+[lib]
+path = "src/lib.rs"
+
+[[bin]]
+name = "text-generation-benchmark"
+path = "src/main.rs"
+
+[dependencies]
+average = "0.13"
+clap = { version = "4.1.4", features = ["derive", "env"] }
+crossterm = "0.26"
+float-ord = "0.3.2"
+serde = {version = "1.0.142", features = ["derive"]}
+serde_json = "1.0"
+text-generation-client = { path = "../router/client" }
+thiserror = "1.0.38"
+tokenizers = "0.13.3"
+tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
+tui = {package = "ratatui", version = "0.20", default-features = false, features = ["crossterm"]}
+tracing = "0.1.37"
+tracing-subscriber = { version = "0.3.16", features = ["json", "env-filter"] }
+
diff --git a/benchmark/rust-toolchain.toml b/benchmark/rust-toolchain.toml
new file mode 100644
index 0000000000000000000000000000000000000000..dcdb06e98eb4ceabec3dcbe9fa03b4ccb148f12f
--- /dev/null
+++ b/benchmark/rust-toolchain.toml
@@ -0,0 +1,3 @@
+[toolchain]
+channel = "1.67.0"
+components = ["rustfmt", "clippy"]
\ No newline at end of file
diff --git a/benchmark/src/app.rs b/benchmark/src/app.rs
new file mode 100644
index 0000000000000000000000000000000000000000..85026a338c24de52f537837425b07721f2822277
--- /dev/null
+++ b/benchmark/src/app.rs
@@ -0,0 +1,688 @@
+/// Inspired by https://github.com/hatoo/oha/blob/bb989ea3cd77727e7743e7daa60a19894bb5e901/src/monitor.rs
+use crate::generation::{Decode, Message, Prefill};
+use crossterm::event::{KeyCode, KeyEvent, KeyModifiers};
+use text_generation_client::ClientError;
+use tokio::sync::mpsc;
+use tui::backend::Backend;
+use tui::layout::{Alignment, Constraint, Direction, Layout};
+use tui::style::{Color, Modifier, Style};
+use tui::text::{Span, Spans};
+use tui::widgets::{
+    Axis, BarChart, Block, Borders, Chart, Dataset, Gauge, GraphType, Paragraph, Tabs,
+};
+use tui::{symbols, Frame};
+
+/// TUI powered App
+pub(crate) struct App {
+    pub(crate) running: bool,
+    completed_runs: Vec<usize>,
+    completed_batch: usize,
+    current_batch: usize,
+    current_tab: usize,
+    touched_tab: bool,
+    zoom: bool,
+    is_error: bool,
+    data: Data,
+    tokenizer_name: String,
+    sequence_length: u32,
+    decode_length: u32,
+    n_run: usize,
+    batch_size: Vec<u32>,
+    receiver: mpsc::Receiver<Result<Message, ClientError>>,
+}
+
+impl App {
+    pub(crate) fn new(
+        receiver: mpsc::Receiver<Result<Message, ClientError>>,
+        tokenizer_name: String,
+        sequence_length: u32,
+        decode_length: u32,
+        n_run: usize,
+        batch_size: Vec<u32>,
+    ) -> Self {
+        let data = Data::new(n_run, batch_size.len());
+        let current_tab = 0;
+
+        let completed_runs: Vec<usize> = (0..batch_size.len()).map(|_| 0).collect();
+        let completed_batch = 0;
+        let current_batch = 0;
+        let is_error = false;
+
+        Self {
+            running: true,
+            completed_runs,
+            completed_batch,
+            current_batch,
+            current_tab,
+            touched_tab: false,
+            zoom: false,
+            is_error,
+            data,
+            tokenizer_name,
+            sequence_length,
+            decode_length,
+            n_run,
+            batch_size,
+            receiver,
+        }
+    }
+
+    /// Handle crossterm key events
+    pub(crate) fn handle_key_event(&mut self, key_event: KeyEvent) {
+        match key_event {
+            // Increase and wrap tab
+            KeyEvent {
+                code: KeyCode::Right,
+                ..
+            }
+            | KeyEvent {
+                code: KeyCode::Tab, ..
+            } => {
+                self.touched_tab = true;
+                self.current_tab = (self.current_tab + 1) % self.batch_size.len();
+            }
+            // Decrease and wrap tab
+            KeyEvent {
+                code: KeyCode::Left,
+                ..
+            } => {
+                self.touched_tab = true;
+                if self.current_tab > 0 {
+                    self.current_tab -= 1;
+                } else {
+                    self.current_tab = self.batch_size.len() - 1;
+                }
+            }
+            // Zoom on throughput/latency fig
+            KeyEvent {
+                code: KeyCode::Char('+'),
+                ..
+            } => {
+                self.zoom = true;
+            }
+            // Unzoom on throughput/latency fig
+            KeyEvent {
+                code: KeyCode::Char('-'),
+                ..
+            } => {
+                self.zoom = false;
+            }
+            // Quit
+            KeyEvent {
+                code: KeyCode::Char('q'),
+                ..
+            }
+            | KeyEvent {
+                code: KeyCode::Char('c'),
+                modifiers: KeyModifiers::CONTROL,
+                ..
+            } => {
+                self.running = false;
+            }
+            _ => (),
+        }
+    }
+
+    /// Get all pending messages from generation task
+    pub(crate) fn tick(&mut self) {
+        while let Ok(message) = self.receiver.try_recv() {
+            match message {
+                Ok(message) => match message {
+                    Message::Prefill(step) => self.data.push_prefill(step, self.current_batch),
+                    Message::Decode(step) => self.data.push_decode(step, self.current_batch),
+                    Message::EndRun => {
+                        self.completed_runs[self.current_batch] += 1;
+                    }
+                    Message::EndBatch => {
+                        self.data.end_batch(self.current_batch);
+                        self.completed_batch += 1;
+
+                        if self.current_batch < self.batch_size.len() - 1 {
+                            // Only go to next tab if the user never touched the tab keys
+                            if !self.touched_tab {
+                                self.current_tab += 1;
+                            }
+
+                            self.current_batch += 1;
+                        }
+                    }
+                    Message::Warmup => {}
+                },
+                Err(_) => self.is_error = true,
+            }
+        }
+    }
+
+    /// Render frame
+    pub fn render<B: Backend>(&mut self, f: &mut Frame<'_, B>) {
+        let batch_progress =
+            (self.completed_batch as f64 / self.batch_size.len() as f64).clamp(0.0, 1.0);
+        let run_progress =
+            (self.completed_runs[self.current_batch] as f64 / self.n_run as f64).clamp(0.0, 1.0);
+
+        // Vertical layout
+        let row5 = Layout::default()
+            .direction(Direction::Vertical)
+            .constraints(
+                [
+                    Constraint::Length(1),
+                    Constraint::Length(3),
+                    Constraint::Length(3),
+                    Constraint::Length(13),
+                    Constraint::Min(10),
+                ]
+                .as_ref(),
+            )
+            .split(f.size());
+
+        // Top row horizontal layout
+        let top = Layout::default()
+            .direction(Direction::Horizontal)
+            .constraints([Constraint::Percentage(50), Constraint::Percentage(50)].as_ref())
+            .split(row5[2]);
+
+        // Mid row horizontal layout
+        let mid = Layout::default()
+            .direction(Direction::Horizontal)
+            .constraints(
+                [
+                    Constraint::Percentage(25),
+                    Constraint::Percentage(25),
+                    Constraint::Percentage(25),
+                    Constraint::Percentage(25),
+                ]
+                .as_ref(),
+            )
+            .split(row5[3]);
+
+        // Left mid row vertical layout
+        let prefill_text = Layout::default()
+            .direction(Direction::Vertical)
+            .constraints([Constraint::Length(8), Constraint::Length(5)].as_ref())
+            .split(mid[0]);
+
+        // Right mid row vertical layout
+        let decode_text = Layout::default()
+            .direction(Direction::Vertical)
+            .constraints([Constraint::Length(8), Constraint::Length(5)].as_ref())
+            .split(mid[2]);
+        let decode_text_latency = Layout::default()
+            .direction(Direction::Horizontal)
+            .constraints([Constraint::Percentage(50), Constraint::Percentage(50)].as_ref())
+            .split(decode_text[0]);
+
+        // Bottom row horizontal layout
+        let bottom = Layout::default()
+            .direction(Direction::Horizontal)
+            .constraints([Constraint::Percentage(50), Constraint::Percentage(50)].as_ref())
+            .split(row5[4]);
+
+        // Title
+        let title = Block::default()
+            .borders(Borders::NONE)
+            .title(format!(
+                "Model: {} | Sequence Length: {} | Decode Length: {}",
+                self.tokenizer_name, self.sequence_length, self.decode_length
+            ))
+            .style(
+                Style::default()
+                    .add_modifier(Modifier::BOLD)
+                    .fg(Color::White),
+            );
+        f.render_widget(title, row5[0]);
+
+        // Helper
+        let helper = Block::default()
+            .borders(Borders::NONE)
+            .title("<- | tab | ->: change batch tab | q / CTRL + c: quit | +/-: zoom")
+            .title_alignment(Alignment::Right)
+            .style(Style::default().fg(Color::White));
+        f.render_widget(helper, row5[0]);
+
+        // Batch tabs
+        let titles = self
+            .batch_size
+            .iter()
+            .map(|b| {
+                Spans::from(vec![Span::styled(
+                    format!("Batch: {b}"),
+                    Style::default().fg(Color::White),
+                )])
+            })
+            .collect();
+        let tabs = Tabs::new(titles)
+            .block(Block::default().borders(Borders::ALL).title("Tabs"))
+            .select(self.current_tab)
+            .style(Style::default().fg(Color::LightCyan))
+            .highlight_style(
+                Style::default()
+                    .add_modifier(Modifier::BOLD)
+                    .bg(Color::Black),
+            );
+        f.render_widget(tabs, row5[1]);
+
+        // Total progress bar
+        let color = if self.is_error {
+            Color::Red
+        } else {
+            Color::LightGreen
+        };
+        let batch_gauge = progress_gauge(
+            "Total Progress",
+            format!("{} / {}", self.completed_batch, self.batch_size.len()),
+            batch_progress,
+            color,
+        );
+        f.render_widget(batch_gauge, top[0]);
+
+        // Batch progress Bar
+        let color = if self.is_error {
+            Color::Red
+        } else {
+            Color::LightBlue
+        };
+        let run_gauge = progress_gauge(
+            "Batch Progress",
+            format!(
+                "{} / {}",
+                self.completed_runs[self.current_batch], self.n_run
+            ),
+            run_progress,
+            color,
+        );
+        f.render_widget(run_gauge, top[1]);
+
+        // Prefill text infos
+        let prefill_latency_block = latency_paragraph(
+            &mut self.data.prefill_latencies[self.current_tab],
+            "Prefill",
+        );
+        let prefill_throughput_block =
+            throughput_paragraph(&self.data.prefill_throughputs[self.current_tab], "Prefill");
+
+        f.render_widget(prefill_latency_block, prefill_text[0]);
+        f.render_widget(prefill_throughput_block, prefill_text[1]);
+
+        // Prefill latency histogram
+        let histo_width = 7;
+        let bins = if mid[1].width < 2 {
+            0
+        } else {
+            (mid[1].width as usize - 2) / (histo_width + 1)
+        }
+        .max(2);
+
+        let histo_data =
+            latency_histogram_data(&self.data.prefill_latencies[self.current_tab], bins);
+        let histo_data_str: Vec<(&str, u64)> =
+            histo_data.iter().map(|(l, v)| (l.as_str(), *v)).collect();
+        let prefill_histogram =
+            latency_histogram(&histo_data_str, "Prefill").bar_width(histo_width as u16);
+        f.render_widget(prefill_histogram, mid[1]);
+
+        // Decode text info
+        let decode_latency_block = latency_paragraph(
+            &mut self.data.decode_latencies[self.current_tab],
+            "Decode Total",
+        );
+        let decode_token_latency_block = latency_paragraph(
+            &mut self.data.decode_token_latencies[self.current_tab],
+            "Decode Token",
+        );
+        let decode_throughput_block =
+            throughput_paragraph(&self.data.decode_throughputs[self.current_tab], "Decode");
+        f.render_widget(decode_latency_block, decode_text_latency[0]);
+        f.render_widget(decode_token_latency_block, decode_text_latency[1]);
+        f.render_widget(decode_throughput_block, decode_text[1]);
+
+        // Decode latency histogram
+        let histo_data =
+            latency_histogram_data(&self.data.decode_latencies[self.current_tab], bins);
+        let histo_data_str: Vec<(&str, u64)> =
+            histo_data.iter().map(|(l, v)| (l.as_str(), *v)).collect();
+        let decode_histogram =
+            latency_histogram(&histo_data_str, "Decode").bar_width(histo_width as u16);
+        f.render_widget(decode_histogram, mid[3]);
+
+        // Prefill latency/throughput chart
+        let prefill_latency_throughput_chart = latency_throughput_chart(
+            &self.data.prefill_batch_latency_throughput,
+            &self.batch_size,
+            self.zoom,
+            "Prefill",
+        );
+        f.render_widget(prefill_latency_throughput_chart, bottom[0]);
+
+        // Decode latency/throughput chart
+        let decode_latency_throughput_chart = latency_throughput_chart(
+            &self.data.decode_batch_latency_throughput,
+            &self.batch_size,
+            self.zoom,
+            "Decode",
+        );
+        f.render_widget(decode_latency_throughput_chart, bottom[1]);
+    }
+}
+
+/// App internal data struct
+struct Data {
+    prefill_latencies: Vec<Vec<f64>>,
+    prefill_throughputs: Vec<Vec<f64>>,
+    decode_latencies: Vec<Vec<f64>>,
+    decode_token_latencies: Vec<Vec<f64>>,
+    decode_throughputs: Vec<Vec<f64>>,
+    prefill_batch_latency_throughput: Vec<(f64, f64)>,
+    decode_batch_latency_throughput: Vec<(f64, f64)>,
+}
+
+impl Data {
+    fn new(n_run: usize, n_batch: usize) -> Self {
+        let prefill_latencies: Vec<Vec<f64>> =
+            (0..n_batch).map(|_| Vec::with_capacity(n_run)).collect();
+        let prefill_throughputs: Vec<Vec<f64>> = prefill_latencies.clone();
+
+        let decode_latencies: Vec<Vec<f64>> = prefill_latencies.clone();
+        let decode_token_latencies: Vec<Vec<f64>> = decode_latencies.clone();
+        let decode_throughputs: Vec<Vec<f64>> = prefill_throughputs.clone();
+
+        let prefill_batch_latency_throughput: Vec<(f64, f64)> = Vec::with_capacity(n_batch);
+        let decode_batch_latency_throughput: Vec<(f64, f64)> =
+            prefill_batch_latency_throughput.clone();
+
+        Self {
+            prefill_latencies,
+            prefill_throughputs,
+            decode_latencies,
+            decode_token_latencies,
+            decode_throughputs,
+            prefill_batch_latency_throughput,
+            decode_batch_latency_throughput,
+        }
+    }
+
+    fn push_prefill(&mut self, prefill: Prefill, batch_idx: usize) {
+        let latency = prefill.latency.as_millis() as f64;
+        self.prefill_latencies[batch_idx].push(latency);
+        self.prefill_throughputs[batch_idx].push(prefill.throughput);
+    }
+
+    fn push_decode(&mut self, decode: Decode, batch_idx: usize) {
+        let latency = decode.latency.as_millis() as f64;
+        let token_latency = decode.token_latency.as_millis() as f64;
+        self.decode_latencies[batch_idx].push(latency);
+        self.decode_token_latencies[batch_idx].push(token_latency);
+        self.decode_throughputs[batch_idx].push(decode.throughput);
+    }
+
+    fn end_batch(&mut self, batch_idx: usize) {
+        self.prefill_batch_latency_throughput.push((
+            self.prefill_latencies[batch_idx].iter().sum::<f64>()
+                / self.prefill_latencies[batch_idx].len() as f64,
+            self.prefill_throughputs[batch_idx].iter().sum::<f64>()
+                / self.prefill_throughputs[batch_idx].len() as f64,
+        ));
+        self.decode_batch_latency_throughput.push((
+            self.decode_latencies[batch_idx].iter().sum::<f64>()
+                / self.decode_latencies[batch_idx].len() as f64,
+            self.decode_throughputs[batch_idx].iter().sum::<f64>()
+                / self.decode_throughputs[batch_idx].len() as f64,
+        ));
+    }
+}
+
+/// Progress bar
+fn progress_gauge(title: &str, label: String, progress: f64, color: Color) -> Gauge {
+    Gauge::default()
+        .block(Block::default().title(title).borders(Borders::ALL))
+        .gauge_style(Style::default().fg(color))
+        .label(Span::raw(label))
+        .ratio(progress)
+}
+
+/// Throughput paragraph
+fn throughput_paragraph<'a>(throughput: &Vec<f64>, name: &'static str) -> Paragraph<'a> {
+    // Throughput average/high/low texts
+    let throughput_texts = statis_spans(throughput, "tokens/secs");
+
+    // Throughput block
+    Paragraph::new(throughput_texts).block(
+        Block::default()
+            .title(Span::raw(format!("{name} Throughput")))
+            .borders(Borders::ALL),
+    )
+}
+
+/// Latency paragraph
+fn latency_paragraph<'a>(latency: &mut Vec<f64>, name: &'static str) -> Paragraph<'a> {
+    // Latency average/high/low texts
+    let mut latency_texts = statis_spans(latency, "ms");
+
+    // Sort latency for percentiles
+    float_ord::sort(latency);
+    let latency_percentiles = crate::utils::percentiles(latency, &[50, 90, 99]);
+
+    // Latency p50/p90/p99 texts
+    let colors = vec![Color::LightGreen, Color::LightYellow, Color::LightRed];
+    for (i, (name, value)) in latency_percentiles.iter().enumerate() {
+        let span = Spans::from(vec![Span::styled(
+            format!("{name}:     {value:.2} ms"),
+            Style::default().fg(colors[i]),
+        )]);
+        latency_texts.push(span);
+    }
+
+    Paragraph::new(latency_texts).block(
+        Block::default()
+            .title(Span::raw(format!("{name} Latency")))
+            .borders(Borders::ALL),
+    )
+}
+
+/// Average/High/Low spans
+fn statis_spans<'a>(data: &Vec<f64>, unit: &'static str) -> Vec<Spans<'a>> {
+    vec![
+        Spans::from(vec![Span::styled(
+            format!(
+                "Average: {:.2} {unit}",
+                data.iter().sum::<f64>() / data.len() as f64
+            ),
+            Style::default().fg(Color::LightBlue),
+        )]),
+        Spans::from(vec![Span::styled(
+            format!(
+                "Lowest:  {:.2} {unit}",
+                data.iter()
+                    .min_by(|a, b| a.total_cmp(b))
+                    .unwrap_or(&std::f64::NAN)
+            ),
+            Style::default().fg(Color::Reset),
+        )]),
+        Spans::from(vec![Span::styled(
+            format!(
+                "Highest: {:.2} {unit}",
+                data.iter()
+                    .max_by(|a, b| a.total_cmp(b))
+                    .unwrap_or(&std::f64::NAN)
+            ),
+            Style::default().fg(Color::Reset),
+        )]),
+    ]
+}
+
+/// Latency histogram data
+fn latency_histogram_data(latency: &[f64], bins: usize) -> Vec<(String, u64)> {
+    let histo_data: Vec<(String, u64)> = {
+        let histo = crate::utils::histogram(latency, bins);
+        histo
+            .into_iter()
+            .map(|(label, v)| (format!("{label:.2}"), v as u64))
+            .collect()
+    };
+
+    histo_data
+}
+
+/// Latency Histogram
+fn latency_histogram<'a>(
+    histo_data_str: &'a Vec<(&'a str, u64)>,
+    name: &'static str,
+) -> BarChart<'a> {
+    BarChart::default()
+        .block(
+            Block::default()
+                .title(format!("{name} latency histogram"))
+                .style(Style::default().fg(Color::LightYellow).bg(Color::Reset))
+                .borders(Borders::ALL),
+        )
+        .data(histo_data_str.as_slice())
+}
+
+/// Latency/Throughput chart
+fn latency_throughput_chart<'a>(
+    latency_throughput: &'a Vec<(f64, f64)>,
+    batch_sizes: &'a [u32],
+    zoom: bool,
+    name: &'static str,
+) -> Chart<'a> {
+    let latency_iter = latency_throughput.iter().map(|(l, _)| l);
+    let throughput_iter = latency_throughput.iter().map(|(_, t)| t);
+
+    // Get extreme values
+    let min_latency: f64 = *latency_iter
+        .clone()
+        .min_by(|a, b| a.total_cmp(b))
+        .unwrap_or(&std::f64::NAN);
+    let max_latency: f64 = *latency_iter
+        .max_by(|a, b| a.total_cmp(b))
+        .unwrap_or(&std::f64::NAN);
+    let min_throughput: f64 = *throughput_iter
+        .clone()
+        .min_by(|a, b| a.total_cmp(b))
+        .unwrap_or(&std::f64::NAN);
+    let max_throughput: f64 = *throughput_iter
+        .max_by(|a, b| a.total_cmp(b))
+        .unwrap_or(&std::f64::NAN);
+
+    // Char min max values
+    let min_x = if zoom {
+        ((min_latency - 0.05 * min_latency) / 100.0).floor() * 100.0
+    } else {
+        0.0
+    };
+    let max_x = ((max_latency + 0.05 * max_latency) / 100.0).ceil() * 100.0;
+    let step_x = (max_x - min_x) / 4.0;
+
+    // Chart min max values
+    let min_y = if zoom {
+        ((min_throughput - 0.05 * min_throughput) / 100.0).floor() * 100.0
+    } else {
+        0.0
+    };
+    let max_y = ((max_throughput + 0.05 * max_throughput) / 100.0).ceil() * 100.0;
+    let step_y = (max_y - min_y) / 4.0;
+
+    // Labels
+    let mut x_labels = vec![Span::styled(
+        format!("{min_x:.2}"),
+        Style::default()
+            .add_modifier(Modifier::BOLD)
+            .fg(Color::Gray)
+            .bg(Color::Reset),
+    )];
+    for i in 0..3 {
+        x_labels.push(Span::styled(
+            format!("{:.2}", min_x + ((i + 1) as f64 * step_x)),
+            Style::default().fg(Color::Gray).bg(Color::Reset),
+        ));
+    }
+    x_labels.push(Span::styled(
+        format!("{max_x:.2}"),
+        Style::default()
+            .add_modifier(Modifier::BOLD)
+            .fg(Color::Gray)
+            .bg(Color::Reset),
+    ));
+
+    // Labels
+    let mut y_labels = vec![Span::styled(
+        format!("{min_y:.2}"),
+        Style::default()
+            .add_modifier(Modifier::BOLD)
+            .fg(Color::Gray)
+            .bg(Color::Reset),
+    )];
+    for i in 0..3 {
+        y_labels.push(Span::styled(
+            format!("{:.2}", min_y + ((i + 1) as f64 * step_y)),
+            Style::default().fg(Color::Gray).bg(Color::Reset),
+        ));
+    }
+    y_labels.push(Span::styled(
+        format!("{max_y:.2}"),
+        Style::default()
+            .add_modifier(Modifier::BOLD)
+            .fg(Color::Gray)
+            .bg(Color::Reset),
+    ));
+
+    // Chart dataset
+    let colors = color_vec();
+    let datasets: Vec<Dataset> = (0..latency_throughput.len())
+        .map(|i| {
+            let color_idx = i % colors.len();
+
+            Dataset::default()
+                .name(batch_sizes[i].to_string())
+                .marker(symbols::Marker::Block)
+                .style(Style::default().fg(colors[color_idx]))
+                .graph_type(GraphType::Scatter)
+                .data(&latency_throughput[i..(i + 1)])
+        })
+        .collect();
+
+    // Chart
+    Chart::new(datasets)
+        .style(Style::default().fg(Color::Cyan).bg(Color::Reset))
+        .block(
+            Block::default()
+                .title(Span::styled(
+                    format!("{name} throughput over latency"),
+                    Style::default().fg(Color::Gray).bg(Color::Reset),
+                ))
+                .borders(Borders::ALL),
+        )
+        .x_axis(
+            Axis::default()
+                .title("ms")
+                .style(Style::default().fg(Color::Gray).bg(Color::Reset))
+                .labels(x_labels)
+                .bounds([min_x, max_x]),
+        )
+        .y_axis(
+            Axis::default()
+                .title("tokens/secs")
+                .style(Style::default().fg(Color::Gray).bg(Color::Reset))
+                .labels(y_labels)
+                .bounds([min_y, max_y]),
+        )
+}
+
+// Colors for latency/throughput chart
+fn color_vec() -> Vec<Color> {
+    vec![
+        Color::Red,
+        Color::Green,
+        Color::Yellow,
+        Color::Blue,
+        Color::Magenta,
+        Color::Cyan,
+        Color::Gray,
+        Color::DarkGray,
+        Color::LightRed,
+        Color::LightGreen,
+        Color::LightYellow,
+        Color::LightBlue,
+        Color::LightMagenta,
+        Color::LightCyan,
+    ]
+}
diff --git a/benchmark/src/event.rs b/benchmark/src/event.rs
new file mode 100644
index 0000000000000000000000000000000000000000..91ce84000bb330dfab0bf64519cc607308e8f366
--- /dev/null
+++ b/benchmark/src/event.rs
@@ -0,0 +1,65 @@
+/// Inspired by https://github.com/orhun/rust-tui-template/blob/472aa515119d4c94903eac12d9784417281dc7f5/src/event.rs
+use crossterm::event;
+use std::time::{Duration, Instant};
+use tokio::sync::{broadcast, mpsc};
+
+/// Events
+#[derive(Debug)]
+pub(crate) enum Event {
+    /// Terminal tick.
+    Tick,
+    /// Key press.
+    Key(event::KeyEvent),
+    /// Terminal resize.
+    Resize(u16, u16),
+}
+
+pub(crate) async fn terminal_event_task(
+    fps: u32,
+    event_sender: mpsc::Sender<Event>,
+    mut shutdown_receiver: broadcast::Receiver<()>,
+    _shutdown_guard_sender: mpsc::Sender<()>,
+) {
+    // End task if a message is received on shutdown_receiver
+    // _shutdown_guard_sender will be dropped once the task is finished
+    tokio::select! {
+        _ = event_loop(fps, event_sender)  => {
+        },
+        _ = shutdown_receiver.recv() => {}
+    }
+}
+
+/// Main event loop
+async fn event_loop(fps: u32, event_sender: mpsc::Sender<Event>) {
+    // Frame budget
+    let per_frame = Duration::from_secs(1) / fps;
+
+    // When was last frame executed
+    let mut last_frame = Instant::now();
+
+    loop {
+        // Sleep to avoid blocking the thread for too long
+        if let Some(sleep) = per_frame.checked_sub(last_frame.elapsed()) {
+            tokio::time::sleep(sleep).await;
+        }
+
+        // Get crossterm event and send a new one over the channel
+        if event::poll(Duration::from_secs(0)).expect("no events available") {
+            match event::read().expect("unable to read event") {
+                event::Event::Key(e) => event_sender.send(Event::Key(e)).await.unwrap_or(()),
+                event::Event::Resize(w, h) => {
+                    event_sender.send(Event::Resize(w, h)).await.unwrap_or(())
+                }
+                _ => (),
+            }
+        }
+
+        // Frame budget exceeded
+        if last_frame.elapsed() >= per_frame {
+            // Send tick
+            event_sender.send(Event::Tick).await.unwrap_or(());
+            // Rest last_frame time
+            last_frame = Instant::now();
+        }
+    }
+}
diff --git a/benchmark/src/generation.rs b/benchmark/src/generation.rs
new file mode 100644
index 0000000000000000000000000000000000000000..4a119e867e1780fa50177a2a5e05efadc542e93d
--- /dev/null
+++ b/benchmark/src/generation.rs
@@ -0,0 +1,226 @@
+use std::time::{Duration, Instant};
+use text_generation_client::{
+    Batch, ClientError, NextTokenChooserParameters, Request, ShardedClient,
+    StoppingCriteriaParameters,
+};
+use tokenizers::{Tokenizer, TruncationDirection};
+use tokio::sync::{broadcast, mpsc};
+
+const LOREM_IPSUM: &str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.";
+
+#[derive(Debug, Clone)]
+pub(crate) struct Prefill {
+    pub(crate) latency: Duration,
+    pub(crate) throughput: f64,
+}
+
+#[derive(Debug, Clone)]
+pub(crate) struct Decode {
+    pub(crate) latency: Duration,
+    pub(crate) token_latency: Duration,
+    pub(crate) throughput: f64,
+}
+
+#[derive(Debug)]
+pub(crate) enum Message {
+    Warmup,
+    Prefill(Prefill),
+    Decode(Decode),
+    EndRun,
+    EndBatch,
+}
+
+/// Benchmarking task
+#[allow(clippy::too_many_arguments)]
+pub(crate) async fn generation_task(
+    tokenizer: Tokenizer,
+    batch_size: Vec<u32>,
+    sequence_length: u32,
+    decode_length: u32,
+    n_runs: usize,
+    warmups: usize,
+    client: ShardedClient,
+    run_sender: mpsc::Sender<Result<Message, ClientError>>,
+    mut shutdown_receiver: broadcast::Receiver<()>,
+    _shutdown_guard_sender: mpsc::Sender<()>,
+) {
+    // End task if a message is received on shutdown_receiver
+    // _shutdown_guard_sender will be dropped once the task is finished
+    tokio::select! {
+        res = generate_runs(tokenizer, batch_size, sequence_length, decode_length, n_runs, warmups, client, run_sender.clone())  => {
+            if let Err(err) = res {
+                run_sender.send(Err(err)).await.unwrap_or(());
+            }
+        },
+        _ = shutdown_receiver.recv() => {}
+    }
+}
+
+/// Benchmark prefill/decode
+#[allow(clippy::too_many_arguments)]
+async fn generate_runs(
+    tokenizer: Tokenizer,
+    batch_size: Vec<u32>,
+    sequence_length: u32,
+    decode_length: u32,
+    n_runs: usize,
+    warmups: usize,
+    mut client: ShardedClient,
+    run_sender: mpsc::Sender<Result<Message, ClientError>>,
+) -> Result<(), ClientError> {
+    // Create a dummy sequence
+    let sequence = create_sequence(sequence_length, tokenizer);
+
+    for b in batch_size {
+        // Warmups on batch size
+        for _ in 0..warmups {
+            let (_, decode_batch) = prefill(
+                sequence.clone(),
+                sequence_length,
+                b,
+                decode_length,
+                &mut client,
+            )
+            .await?;
+            let _ = decode(decode_batch, &mut client).await?;
+            // Send warmup message
+            run_sender.send(Ok(Message::Warmup)).await.unwrap_or(());
+        }
+
+        for _ in 0..n_runs {
+            let (prefill, decode_batch) = prefill(
+                sequence.clone(),
+                sequence_length,
+                b,
+                decode_length,
+                &mut client,
+            )
+            .await?;
+            // Send prefill message
+            run_sender
+                .send(Ok(Message::Prefill(prefill)))
+                .await
+                .unwrap_or(());
+
+            let decode = decode(decode_batch, &mut client).await?;
+
+            // Send decode message
+            run_sender
+                .send(Ok(Message::Decode(decode)))
+                .await
+                .unwrap_or(());
+
+            // Send run ended message
+            run_sender.send(Ok(Message::EndRun)).await.unwrap_or(());
+        }
+        // Batch ended
+        run_sender.send(Ok(Message::EndBatch)).await.unwrap_or(());
+    }
+    Ok(())
+}
+
+// Run a prefill step
+async fn prefill(
+    sequence: String,
+    sequence_length: u32,
+    batch_size: u32,
+    decode_length: u32,
+    client: &mut ShardedClient,
+) -> Result<(Prefill, Batch), ClientError> {
+    // Create requests
+    let requests = (0..batch_size)
+        .map(|id| Request {
+            id: id.into(),
+            inputs: sequence.clone(),
+            truncate: sequence_length,
+            parameters: Some(NextTokenChooserParameters {
+                temperature: 1.0,
+                top_k: 0,
+                top_p: 1.0,
+                typical_p: 1.0,
+                do_sample: false,
+                seed: 0,
+                repetition_penalty: 1.0,
+                watermark: false,
+            }),
+            stopping_parameters: Some(StoppingCriteriaParameters {
+                max_new_tokens: decode_length,
+                stop_sequences: vec![],
+                ignore_eos_token: true, // Will not stop even if a eos token is generated
+            }),
+        })
+        .collect();
+
+    let batch = Batch {
+        id: 0,
+        requests,
+        size: batch_size,
+        max_tokens: batch_size * (sequence_length + decode_length),
+    };
+
+    // Run prefill
+    let start_time = Instant::now();
+    let (_, decode_batch) = client.prefill(batch.clone()).await?;
+
+    // Get latency
+    let latency = start_time.elapsed();
+
+    // Compute throughput from latency and batch size
+    let throughput = batch_size as f64 / latency.as_secs_f64();
+
+    // Decode batch cannot be empty
+    let decode_batch = decode_batch.expect("decode_batch is None. This is a bug.");
+
+    let step = Prefill {
+        latency,
+        throughput,
+    };
+
+    Ok((step, decode_batch))
+}
+
+/// Run a full decode
+async fn decode(batch: Batch, client: &mut ShardedClient) -> Result<Decode, ClientError> {
+    let mut decode_length = 0;
+    let batch_size = batch.size;
+
+    let start_time = Instant::now();
+
+    // Full decode over decode length
+    let mut next_batch = Some(batch);
+    while let Some(batch) = next_batch {
+        let result = client.decode(vec![batch]).await?;
+        next_batch = result.1;
+        decode_length += 1;
+    }
+
+    // Get latency
+    let latency = start_time.elapsed();
+    let token_latency = latency / decode_length;
+
+    // Compute throughput from latency, batch size and decode length
+    let throughput = (batch_size * decode_length) as f64 / latency.as_secs_f64();
+
+    let step = Decode {
+        latency,
+        token_latency,
+        throughput,
+    };
+    Ok(step)
+}
+
+/// Create a dummy sequence of the correct length
+fn create_sequence(sequence_length: u32, tokenizer: Tokenizer) -> String {
+    let lorem_ipsum_length = tokenizer.encode(LOREM_IPSUM, true).unwrap().len();
+    // Repeat lorem ipsum to cover sequence length
+    let string_sequence =
+        LOREM_IPSUM.repeat((0..sequence_length).step_by(lorem_ipsum_length).len());
+    // Encode sequence
+    let mut encoding = tokenizer.encode(string_sequence, true).unwrap();
+    // Truncate to sequence_length
+    encoding.truncate(sequence_length as usize, 0, TruncationDirection::Left);
+    // Decode
+    tokenizer
+        .decode(Vec::from(encoding.get_ids()), false)
+        .unwrap()
+}
diff --git a/benchmark/src/lib.rs b/benchmark/src/lib.rs
new file mode 100644
index 0000000000000000000000000000000000000000..4da0b573c213b2099bb314fab5ce1ce6cc2ed974
--- /dev/null
+++ b/benchmark/src/lib.rs
@@ -0,0 +1,110 @@
+mod app;
+mod event;
+mod generation;
+mod utils;
+
+use crate::app::App;
+use crate::event::Event;
+use crossterm::ExecutableCommand;
+use std::io;
+use text_generation_client::ShardedClient;
+use tokenizers::Tokenizer;
+use tokio::sync::{broadcast, mpsc};
+use tui::backend::CrosstermBackend;
+use tui::Terminal;
+
+/// Run benchmarking app
+#[allow(clippy::too_many_arguments)]
+pub async fn run(
+    tokenizer_name: String,
+    tokenizer: Tokenizer,
+    batch_size: Vec<u32>,
+    sequence_length: u32,
+    decode_length: u32,
+    n_runs: usize,
+    warmups: usize,
+    client: ShardedClient,
+) -> Result<(), crossterm::ErrorKind> {
+    // Initialize terminal properties
+    crossterm::terminal::enable_raw_mode()?;
+    io::stdout().execute(crossterm::terminal::EnterAlternateScreen)?;
+    io::stdout().execute(crossterm::cursor::Hide)?;
+
+    // Initialize terminal
+    let mut terminal = {
+        let backend = CrosstermBackend::new(io::stdout());
+        Terminal::new(backend)?
+    };
+
+    // Create message channel between generation_task and app
+    let (run_sender, run_receiver) = mpsc::channel(8);
+    // Crossterm event channel
+    let (event_sender, mut event_receiver) = mpsc::channel(8);
+    // Shutdown channel to terminate tasks
+    let (shutdown_sender, _) = broadcast::channel(1);
+    // Channel to check if tasks terminated
+    let (shutdown_guard_sender, mut shutdown_guard_receiver) = mpsc::channel(1);
+
+    // Create generation task
+    tokio::spawn(generation::generation_task(
+        tokenizer,
+        batch_size.clone(),
+        sequence_length,
+        decode_length,
+        n_runs,
+        warmups,
+        client,
+        run_sender,
+        shutdown_sender.subscribe(),
+        shutdown_guard_sender.clone(),
+    ));
+
+    // Create event task
+    tokio::spawn(event::terminal_event_task(
+        250,
+        event_sender,
+        shutdown_sender.subscribe(),
+        shutdown_guard_sender.clone(),
+    ));
+
+    // Drop our end of shutdown sender
+    drop(shutdown_guard_sender);
+
+    // Create App
+    let mut app = App::new(
+        run_receiver,
+        tokenizer_name,
+        sequence_length,
+        decode_length,
+        n_runs,
+        batch_size,
+    );
+
+    while app.running {
+        // Draw frame
+        terminal.draw(|frame| app.render(frame))?;
+
+        // Await a new event from event handling task
+        match event_receiver.recv().await {
+            None => break,
+            // Update app state
+            Some(event) => match event {
+                Event::Tick => app.tick(),
+                Event::Key(key_event) => app.handle_key_event(key_event),
+                _ => {}
+            },
+        }
+    }
+
+    // Ask tasks to shutdown
+    let _ = shutdown_sender.send(());
+    // Wait for tasks to shutdown
+    let _ = shutdown_guard_receiver.recv().await;
+
+    // Revert terminal to original view
+    io::stdout().execute(crossterm::terminal::LeaveAlternateScreen)?;
+    crossterm::terminal::disable_raw_mode()?;
+    io::stdout().execute(crossterm::cursor::Show)?;
+
+    Ok(())
+}
diff --git a/benchmark/src/main.rs b/benchmark/src/main.rs
new file mode 100644
index 0000000000000000000000000000000000000000..03f61dcdd04affd829901606b29e94e996a79309
--- /dev/null
+++ b/benchmark/src/main.rs
@@ -0,0 +1,131 @@
+/// Text Generation Inference benchmarking tool
+///
+/// Inspired by the great Oha app: https://github.com/hatoo/oha
+/// and: https://github.com/orhun/rust-tui-template
+use clap::Parser;
+use std::path::Path;
+use text_generation_client::ShardedClient;
+use tokenizers::{FromPretrainedParameters, Tokenizer};
+use tracing_subscriber::layer::SubscriberExt;
+use tracing_subscriber::util::SubscriberInitExt;
+use tracing_subscriber::EnvFilter;
+
+/// App Configuration
+#[derive(Parser, Debug)]
+#[clap(author, version, about, long_about = None)]
+struct Args {
+    #[clap(short, long, env)]
+    tokenizer_name: String,
+    #[clap(default_value = "main", long, env)]
+    revision: String,
+    #[clap(short, long)]
+    batch_size: Option<Vec<u32>>,
+    #[clap(default_value = "10", short, long, env)]
+    sequence_length: u32,
+    #[clap(default_value = "8", short, long, env)]
+    decode_length: u32,
+    #[clap(default_value = "10", short, long, env)]
+    runs: usize,
+    #[clap(default_value = "1", short, long, env)]
+    warmups: usize,
+    #[clap(default_value = "/tmp/text-generation-server-0", short, long, env)]
+    master_shard_uds_path: String,
+}
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    // Get args
+    let args = Args::parse();
+    // Pattern match configuration
+    let Args {
+        tokenizer_name,
+        revision,
+        batch_size,
+        sequence_length,
+        decode_length,
+        runs,
+        warmups,
+        master_shard_uds_path,
+    } = args;
+
+    let batch_size = batch_size.unwrap_or(vec![1, 2, 4, 8, 16, 32]);
+
+    init_logging();
+
+    // Tokenizer instance
+    // This will only be used to validate payloads
+    tracing::info!("Loading tokenizer");
+    let local_path = Path::new(&tokenizer_name);
+    let tokenizer =
+        if local_path.exists() && local_path.is_dir() && local_path.join("tokenizer.json").exists()
+        {
+            // Load local tokenizer
+            tracing::info!("Found local tokenizer");
+            Tokenizer::from_file(local_path.join("tokenizer.json")).unwrap()
+        } else {
+            tracing::info!("Downloading tokenizer");
+
+            // Parse Huggingface hub token
+            let auth_token = std::env::var("HUGGING_FACE_HUB_TOKEN").ok();
+
+            // Download and instantiate tokenizer
+            // We need to download it outside of the Tokio runtime
+            let params = FromPretrainedParameters {
+                revision,
+                auth_token,
+                ..Default::default()
+            };
+            Tokenizer::from_pretrained(tokenizer_name.clone(), Some(params)).unwrap()
+        };
+    tracing::info!("Tokenizer loaded");
+
+    // Launch Tokio runtime
+    tokio::runtime::Builder::new_multi_thread()
+        .enable_all()
+        .build()
+        .unwrap()
+        .block_on(async {
+            // Instantiate sharded client from the master unix socket
+            tracing::info!("Connect to model server");
+            let mut sharded_client = ShardedClient::connect_uds(master_shard_uds_path)
+                .await
+                .expect("Could not connect to server");
+            // Clear the cache; useful if the webserver rebooted
+            sharded_client
+                .clear_cache(None)
+                .await
+                .expect("Unable to clear cache");
+            tracing::info!("Connected");
+
+            // Run app
+            text_generation_benchmark::run(
+                tokenizer_name,
+                tokenizer,
+                batch_size,
+                sequence_length,
+                decode_length,
+                runs,
+                warmups,
+                sharded_client,
+            )
+            .await
+            .unwrap();
+        });
+    Ok(())
+}
+
+/// Init logging using LOG_LEVEL
+fn init_logging() {
+    // STDOUT/STDERR layer
+    let fmt_layer = tracing_subscriber::fmt::layer()
+        .with_file(true)
+        .with_line_number(true);
+
+    // Filter events with LOG_LEVEL
+    let env_filter =
+        EnvFilter::try_from_env("LOG_LEVEL").unwrap_or_else(|_| EnvFilter::new("info"));
+
+    tracing_subscriber::registry()
+        .with(env_filter)
+        .with(fmt_layer)
+        .init();
+}
diff --git a/benchmark/src/utils.rs b/benchmark/src/utils.rs
new file mode 100644
index 0000000000000000000000000000000000000000..d096d65510f476b40b7c4af705ffc5141a81cb7d
--- /dev/null
+++ b/benchmark/src/utils.rs
@@ -0,0 +1,43 @@
+/// MIT License
+//
+// Copyright (c) 2020 hatoo
+//
+// Permission is hereby granted, free of charge, to any person obtaining a copy
+// of this software and associated documentation files (the "Software"), to deal
+// in the Software without restriction, including without limitation the rights
+// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the Software is
+// furnished to do so, subject to the following conditions:
+//
+// The above copyright notice and this permission notice shall be included in all
+// copies or substantial portions of the Software.
+use std::collections::BTreeMap;
+
+pub(crate) fn histogram(values: &[f64], bins: usize) -> Vec<(f64, usize)> {
+    assert!(bins >= 2);
+    let mut bucket: Vec<usize> = vec![0; bins];
+    let min = values.iter().collect::<average::Min>().min();
+    let max = values.iter().collect::<average::Max>().max();
+    let step = (max - min) / (bins - 1) as f64;
+
+    for &v in values {
+        let i = std::cmp::min(((v - min) / step).ceil() as usize, bins - 1);
+        bucket[i] += 1;
+    }
+
+    bucket
+        .into_iter()
+        .enumerate()
+        .map(|(i, v)| (min + step * i as f64, v))
+        .collect()
+}
+
+pub(crate) fn percentiles(values: &[f64], pecents: &[i32]) -> BTreeMap<String, f64> {
+    pecents
+        .iter()
+        .map(|&p| {
+            let i = (f64::from(p) / 100.0 * values.len() as f64) as usize;
+            (format!("p{p}"), *values.get(i).unwrap_or(&std::f64::NAN))
+        })
+        .collect()
+}
diff --git a/clients/python/Makefile b/clients/python/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..8b4334bd5701d9850b36eefb4a860ea8fc0eee24
--- /dev/null
+++ b/clients/python/Makefile
@@ -0,0 +1,6 @@
+unit-tests:
+	python -m pytest --cov=text_generation tests
+
+install:
+	pip install pip --upgrade
+	pip install -e .
\ No newline at end of file
diff --git a/clients/python/poetry.lock b/clients/python/poetry.lock
new file mode 100644
index 0000000000000000000000000000000000000000..1503c40eb8801e86ce6d8975b9b720f39c76ab94
--- /dev/null
+++ b/clients/python/poetry.lock
@@ -0,0 +1,1038 @@
+[[package]]
+name = "aiohttp"
+version = "3.8.4"
+description = "Async http client/server framework (asyncio)"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+aiosignal = ">=1.1.2"
+async-timeout = ">=4.0.0a3,<5.0"
+asynctest = {version = "0.13.0", markers = "python_version < \"3.8\""}
+attrs = ">=17.3.0"
+charset-normalizer = ">=2.0,<4.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
+yarl = ">=1.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli", "aiodns", "cchardet"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
+
+[[package]]
+name = "async-timeout"
+version = "4.0.2"
+description = "Timeout context manager for asyncio programs"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+typing-extensions = {version = ">=3.6.5", markers = "python_version < \"3.8\""}
+
+[[package]]
+name = "asynctest"
+version = "0.13.0"
+description = "Enhance the standard unittest package with features for testing asyncio libraries"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "atomicwrites"
+version = "1.4.1"
+description = "Atomic file writes."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "attrs"
+version = "22.2.0"
+description = "Classes Without Boilerplate"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"]
+dev = ["attrs[docs,tests]"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"]
+tests = ["attrs[tests-no-zope]", "zope.interface"]
+tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=0.971,<0.990)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests_no_zope = ["cloudpickle", "hypothesis", "mypy (>=0.971,<0.990)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+
+[[package]]
+name = "certifi"
+version = "2022.12.7"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "charset-normalizer"
+version = "3.1.0"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+description = "Cross-platform colored terminal text."
+category = "main"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+
+[[package]]
+name = "coverage"
+version = "7.2.1"
+description = "Code coverage measurement for Python"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
+
+[package.extras]
+toml = ["tomli"]
+
+[[package]]
+name = "filelock"
+version = "3.10.0"
+description = "A platform independent file lock."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo (>=2022.12.7)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.22,!=1.23.4)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.2.1)", "pytest (>=7.2.2)", "pytest-cov (>=4)", "pytest-timeout (>=2.1)"]
+
+[[package]]
+name = "frozenlist"
+version = "1.3.3"
+description = "A list-like structure which implements collections.abc.MutableSequence"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "huggingface-hub"
+version = "0.13.2"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+filelock = "*"
+importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+quality = ["black (>=23.1,<24.0)", "mypy (==0.982)", "ruff (>=0.0.241)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "jedi", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile"]
+torch = ["torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+
+[[package]]
+name = "idna"
+version = "3.4"
+description = "Internationalized Domain Names in Applications (IDNA)"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "importlib-metadata"
+version = "6.0.0"
+description = "Read metadata from Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""}
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+perf = ["ipython"]
+testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"]
+
+[[package]]
+name = "iniconfig"
+version = "2.0.0"
+description = "brain-dead simple config-ini parsing"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "multidict"
+version = "6.0.4"
+description = "multidict implementation"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "packaging"
+version = "23.0"
+description = "Core utilities for Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "pluggy"
+version = "1.0.0"
+description = "plugin and hook calling mechanisms for python"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
+
+[package.extras]
+dev = ["pre-commit", "tox"]
+testing = ["pytest", "pytest-benchmark"]
+
+[[package]]
+name = "py"
+version = "1.11.0"
+description = "library with cross-python path, ini-parsing, io, code, log facilities"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "pydantic"
+version = "1.10.6"
+description = "Data validation and settings management using python type hints"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+typing-extensions = ">=4.2.0"
+
+[package.extras]
+dotenv = ["python-dotenv (>=0.10.4)"]
+email = ["email-validator (>=1.0.3)"]
+
+[[package]]
+name = "pytest"
+version = "6.2.5"
+description = "pytest: simple powerful testing with Python"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""}
+attrs = ">=19.2.0"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
+iniconfig = "*"
+packaging = "*"
+pluggy = ">=0.12,<2.0"
+py = ">=1.8.2"
+toml = "*"
+
+[package.extras]
+testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
+
+[[package]]
+name = "pytest-asyncio"
+version = "0.17.2"
+description = "Pytest support for asyncio"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+pytest = ">=6.1.0"
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.8\""}
+
+[package.extras]
+testing = ["coverage (==6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (==0.931)"]
+
+[[package]]
+name = "pytest-cov"
+version = "3.0.0"
+description = "Pytest plugin for measuring coverage."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+coverage = {version = ">=5.2.1", extras = ["toml"]}
+pytest = ">=4.6"
+
+[package.extras]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
+
+[[package]]
+name = "PyYAML"
+version = "6.0"
+description = "YAML parser and emitter for Python"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "requests"
+version = "2.28.2"
+description = "Python HTTP for Humans."
+category = "main"
+optional = false
+python-versions = ">=3.7, <4"
+
+[package.dependencies]
+certifi = ">=2017.4.17"
+charset-normalizer = ">=2,<4"
+idna = ">=2.5,<4"
+urllib3 = ">=1.21.1,<1.27"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "toml"
+version = "0.10.2"
+description = "Python Library for Tom's Obvious, Minimal Language"
+category = "dev"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+
+[[package]]
+name = "tomli"
+version = "2.0.1"
+description = "A lil' TOML parser"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "tqdm"
+version = "4.65.0"
+description = "Fast, Extensible Progress Meter"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["py-make (>=0.1.0)", "twine", "wheel"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.5.0"
+description = "Backported and Experimental Type Hints for Python 3.7+"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "urllib3"
+version = "1.26.15"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
+secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
+socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+
+[[package]]
+name = "yarl"
+version = "1.8.2"
+description = "Yet another URL library"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
+
+[[package]]
+name = "zipp"
+version = "3.15.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+
+[metadata]
+lock-version = "1.1"
+python-versions = "^3.7"
+content-hash = "0db2f97d52c557dd7f90c55b4ad5bbe308c957c5f7f99fec53c57e0a13822cb4"
+
+[metadata.files]
+aiohttp = [
+    {file = "aiohttp-3.8.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:5ce45967538fb747370308d3145aa68a074bdecb4f3a300869590f725ced69c1"},
+    {file = "aiohttp-3.8.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b744c33b6f14ca26b7544e8d8aadff6b765a80ad6164fb1a430bbadd593dfb1a"},
+    {file = "aiohttp-3.8.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1a45865451439eb320784918617ba54b7a377e3501fb70402ab84d38c2cd891b"},
+    {file = "aiohttp-3.8.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a86d42d7cba1cec432d47ab13b6637bee393a10f664c425ea7b305d1301ca1a3"},
+    {file = "aiohttp-3.8.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ee3c36df21b5714d49fc4580247947aa64bcbe2939d1b77b4c8dcb8f6c9faecc"},
+    {file = "aiohttp-3.8.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:176a64b24c0935869d5bbc4c96e82f89f643bcdf08ec947701b9dbb3c956b7dd"},
+    {file = "aiohttp-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c844fd628851c0bc309f3c801b3a3d58ce430b2ce5b359cd918a5a76d0b20cb5"},
+    {file = "aiohttp-3.8.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5393fb786a9e23e4799fec788e7e735de18052f83682ce2dfcabaf1c00c2c08e"},
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diff --git a/clients/python/pyproject.toml b/clients/python/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..00bb99fc584fa5f38f7792af1d689ea1971d75ac
--- /dev/null
+++ b/clients/python/pyproject.toml
@@ -0,0 +1,29 @@
+[tool.poetry]
+name = "text-generation"
+version = "0.5.1"
+description = "Hugging Face Text Generation Python Client"
+license = "Apache-2.0"
+authors = ["Olivier Dehaene <olivier@huggingface.co>"]
+maintainers = ["Olivier Dehaene <olivier@huggingface.co>"]
+readme = "README.md"
+homepage = "https://github.com/huggingface/text-generation-inference"
+repository = "https://github.com/huggingface/text-generation-inference"
+
+
+[tool.poetry.dependencies]
+python = "^3.7"
+pydantic = "^1.10"
+aiohttp = "^3.8"
+huggingface-hub = ">= 0.12, < 1.0"
+
+[tool.poetry.dev-dependencies]
+pytest = "^6.2.5"
+pytest-asyncio = "^0.17.2"
+pytest-cov = "^3.0.0"
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
diff --git a/clients/python/tests/conftest.py b/clients/python/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..48734f0da757da482617ca25c48bf96c5ef27aae
--- /dev/null
+++ b/clients/python/tests/conftest.py
@@ -0,0 +1,51 @@
+import pytest
+
+from text_generation import __version__
+from huggingface_hub.utils import build_hf_headers
+
+
+@pytest.fixture
+def flan_t5_xxl():
+    return "google/flan-t5-xxl"
+
+
+@pytest.fixture
+def fake_model():
+    return "fake/model"
+
+
+@pytest.fixture
+def unsupported_model():
+    return "gpt2"
+
+
+@pytest.fixture
+def base_url():
+    return "https://api-inference.huggingface.co/models"
+
+
+@pytest.fixture
+def bloom_url(base_url, bloom_model):
+    return f"{base_url}/{bloom_model}"
+
+
+@pytest.fixture
+def flan_t5_xxl_url(base_url, flan_t5_xxl):
+    return f"{base_url}/{flan_t5_xxl}"
+
+
+@pytest.fixture
+def fake_url(base_url, fake_model):
+    return f"{base_url}/{fake_model}"
+
+
+@pytest.fixture
+def unsupported_url(base_url, unsupported_model):
+    return f"{base_url}/{unsupported_model}"
+
+
+@pytest.fixture(scope="session")
+def hf_headers():
+    return build_hf_headers(
+        library_name="text-generation-tests", library_version=__version__
+    )
diff --git a/clients/python/tests/test_client.py b/clients/python/tests/test_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..8972dfd1df721033e478e9329714dee5fd893c9a
--- /dev/null
+++ b/clients/python/tests/test_client.py
@@ -0,0 +1,146 @@
+import pytest
+
+from text_generation import Client, AsyncClient
+from text_generation.errors import NotFoundError, ValidationError
+from text_generation.types import FinishReason, PrefillToken, Token
+
+
+def test_generate(flan_t5_xxl_url, hf_headers):
+    client = Client(flan_t5_xxl_url, hf_headers)
+    response = client.generate("test", max_new_tokens=1)
+
+    assert response.generated_text == ""
+    assert response.details.finish_reason == FinishReason.Length
+    assert response.details.generated_tokens == 1
+    assert response.details.seed is None
+    assert len(response.details.prefill) == 1
+    assert response.details.prefill[0] == PrefillToken(id=0, text="<pad>", logprob=None)
+    assert len(response.details.tokens) == 1
+    assert response.details.tokens[0] == Token(
+        id=3, text="", logprob=-1.984375, special=False
+    )
+
+
+def test_generate_best_of(flan_t5_xxl_url, hf_headers):
+    client = Client(flan_t5_xxl_url, hf_headers)
+    response = client.generate("test", max_new_tokens=1, best_of=2, do_sample=True)
+
+    assert response.details.seed is not None
+    assert response.details.best_of_sequences is not None
+    assert len(response.details.best_of_sequences) == 1
+    assert response.details.best_of_sequences[0].seed is not None
+
+
+def test_generate_not_found(fake_url, hf_headers):
+    client = Client(fake_url, hf_headers)
+    with pytest.raises(NotFoundError):
+        client.generate("test")
+
+
+def test_generate_validation_error(flan_t5_xxl_url, hf_headers):
+    client = Client(flan_t5_xxl_url, hf_headers)
+    with pytest.raises(ValidationError):
+        client.generate("test", max_new_tokens=10_000)
+
+
+def test_generate_stream(flan_t5_xxl_url, hf_headers):
+    client = Client(flan_t5_xxl_url, hf_headers)
+    responses = [
+        response for response in client.generate_stream("test", max_new_tokens=1)
+    ]
+
+    assert len(responses) == 1
+    response = responses[0]
+
+    assert response.generated_text == ""
+    assert response.details.finish_reason == FinishReason.Length
+    assert response.details.generated_tokens == 1
+    assert response.details.seed is None
+
+
+def test_generate_stream_not_found(fake_url, hf_headers):
+    client = Client(fake_url, hf_headers)
+    with pytest.raises(NotFoundError):
+        list(client.generate_stream("test"))
+
+
+def test_generate_stream_validation_error(flan_t5_xxl_url, hf_headers):
+    client = Client(flan_t5_xxl_url, hf_headers)
+    with pytest.raises(ValidationError):
+        list(client.generate_stream("test", max_new_tokens=10_000))
+
+
+@pytest.mark.asyncio
+async def test_generate_async(flan_t5_xxl_url, hf_headers):
+    client = AsyncClient(flan_t5_xxl_url, hf_headers)
+    response = await client.generate("test", max_new_tokens=1)
+
+    assert response.generated_text == ""
+    assert response.details.finish_reason == FinishReason.Length
+    assert response.details.generated_tokens == 1
+    assert response.details.seed is None
+    assert len(response.details.prefill) == 1
+    assert response.details.prefill[0] == PrefillToken(id=0, text="<pad>", logprob=None)
+    assert len(response.details.tokens) == 1
+    assert response.details.tokens[0] == Token(
+        id=3, text="", logprob=-1.984375, special=False
+    )
+
+
+@pytest.mark.asyncio
+async def test_generate_async_best_of(flan_t5_xxl_url, hf_headers):
+    client = AsyncClient(flan_t5_xxl_url, hf_headers)
+    response = await client.generate(
+        "test", max_new_tokens=1, best_of=2, do_sample=True
+    )
+
+    assert response.details.seed is not None
+    assert response.details.best_of_sequences is not None
+    assert len(response.details.best_of_sequences) == 1
+    assert response.details.best_of_sequences[0].seed is not None
+
+
+@pytest.mark.asyncio
+async def test_generate_async_not_found(fake_url, hf_headers):
+    client = AsyncClient(fake_url, hf_headers)
+    with pytest.raises(NotFoundError):
+        await client.generate("test")
+
+
+@pytest.mark.asyncio
+async def test_generate_async_validation_error(flan_t5_xxl_url, hf_headers):
+    client = AsyncClient(flan_t5_xxl_url, hf_headers)
+    with pytest.raises(ValidationError):
+        await client.generate("test", max_new_tokens=10_000)
+
+
+@pytest.mark.asyncio
+async def test_generate_stream_async(flan_t5_xxl_url, hf_headers):
+    client = AsyncClient(flan_t5_xxl_url, hf_headers)
+    responses = [
+        response async for response in client.generate_stream("test", max_new_tokens=1)
+    ]
+
+    assert len(responses) == 1
+    response = responses[0]
+
+    assert response.generated_text == ""
+    assert response.details.finish_reason == FinishReason.Length
+    assert response.details.generated_tokens == 1
+    assert response.details.seed is None
+
+
+@pytest.mark.asyncio
+async def test_generate_stream_async_not_found(fake_url, hf_headers):
+    client = AsyncClient(fake_url, hf_headers)
+    with pytest.raises(NotFoundError):
+        async for _ in client.generate_stream("test"):
+            pass
+
+
+@pytest.mark.asyncio
+async def test_generate_stream_async_validation_error(flan_t5_xxl_url, hf_headers):
+    client = AsyncClient(flan_t5_xxl_url, hf_headers)
+    with pytest.raises(ValidationError):
+        async for _ in client.generate_stream("test", max_new_tokens=10_000):
+            pass
diff --git a/clients/python/tests/test_errors.py b/clients/python/tests/test_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..8389ed313812a8e56bef21a5b4437d55911469f3
--- /dev/null
+++ b/clients/python/tests/test_errors.py
@@ -0,0 +1,64 @@
+from text_generation.errors import (
+    parse_error,
+    GenerationError,
+    IncompleteGenerationError,
+    OverloadedError,
+    ValidationError,
+    BadRequestError,
+    ShardNotReadyError,
+    ShardTimeoutError,
+    NotFoundError,
+    RateLimitExceededError,
+    UnknownError,
+)
+
+
+def test_generation_error():
+    payload = {"error_type": "generation", "error": "test"}
+    assert isinstance(parse_error(400, payload), GenerationError)
+
+
+def test_incomplete_generation_error():
+    payload = {"error_type": "incomplete_generation", "error": "test"}
+    assert isinstance(parse_error(400, payload), IncompleteGenerationError)
+
+
+def test_overloaded_error():
+    payload = {"error_type": "overloaded", "error": "test"}
+    assert isinstance(parse_error(400, payload), OverloadedError)
+
+
+def test_validation_error():
+    payload = {"error_type": "validation", "error": "test"}
+    assert isinstance(parse_error(400, payload), ValidationError)
+
+
+def test_bad_request_error():
+    payload = {"error": "test"}
+    assert isinstance(parse_error(400, payload), BadRequestError)
+
+
+def test_shard_not_ready_error():
+    payload = {"error": "test"}
+    assert isinstance(parse_error(403, payload), ShardNotReadyError)
+    assert isinstance(parse_error(424, payload), ShardNotReadyError)
+
+
+def test_shard_timeout_error():
+    payload = {"error": "test"}
+    assert isinstance(parse_error(504, payload), ShardTimeoutError)
+
+
+def test_not_found_error():
+    payload = {"error": "test"}
+    assert isinstance(parse_error(404, payload), NotFoundError)
+
+
+def test_rate_limit_exceeded_error():
+    payload = {"error": "test"}
+    assert isinstance(parse_error(429, payload), RateLimitExceededError)
+
+
+def test_unknown_error():
+    payload = {"error": "test"}
+    assert isinstance(parse_error(500, payload), UnknownError)
diff --git a/clients/python/tests/test_inference_api.py b/clients/python/tests/test_inference_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..59297c26e94b769332a5aeeff7ef81980e70d487
--- /dev/null
+++ b/clients/python/tests/test_inference_api.py
@@ -0,0 +1,42 @@
+import pytest
+
+from text_generation import (
+    InferenceAPIClient,
+    InferenceAPIAsyncClient,
+    Client,
+    AsyncClient,
+)
+from text_generation.errors import NotSupportedError, NotFoundError
+from text_generation.inference_api import check_model_support, deployed_models
+
+
+def test_check_model_support(flan_t5_xxl, unsupported_model, fake_model):
+    assert check_model_support(flan_t5_xxl)
+    assert not check_model_support(unsupported_model)
+
+    with pytest.raises(NotFoundError):
+        check_model_support(fake_model)
+
+
+def test_deployed_models():
+    deployed_models()
+
+
+def test_client(flan_t5_xxl):
+    client = InferenceAPIClient(flan_t5_xxl)
+    assert isinstance(client, Client)
+
+
+def test_client_unsupported_model(unsupported_model):
+    with pytest.raises(NotSupportedError):
+        InferenceAPIClient(unsupported_model)
+
+
+def test_async_client(flan_t5_xxl):
+    client = InferenceAPIAsyncClient(flan_t5_xxl)
+    assert isinstance(client, AsyncClient)
+
+
+def test_async_client_unsupported_model(unsupported_model):
+    with pytest.raises(NotSupportedError):
+        InferenceAPIAsyncClient(unsupported_model)
diff --git a/clients/python/tests/test_types.py b/clients/python/tests/test_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..77689ade7c9f9f0ac0ca8f3772e0d71ce31b7b0a
--- /dev/null
+++ b/clients/python/tests/test_types.py
@@ -0,0 +1,84 @@
+import pytest
+
+from text_generation.types import Parameters, Request
+from text_generation.errors import ValidationError
+
+
+def test_parameters_validation():
+    # Test best_of
+    Parameters(best_of=1)
+    with pytest.raises(ValidationError):
+        Parameters(best_of=0)
+    with pytest.raises(ValidationError):
+        Parameters(best_of=-1)
+    Parameters(best_of=2, do_sample=True)
+    with pytest.raises(ValidationError):
+        Parameters(best_of=2)
+    with pytest.raises(ValidationError):
+        Parameters(best_of=2, seed=1)
+
+    # Test repetition_penalty
+    Parameters(repetition_penalty=1)
+    with pytest.raises(ValidationError):
+        Parameters(repetition_penalty=0)
+    with pytest.raises(ValidationError):
+        Parameters(repetition_penalty=-1)
+
+    # Test seed
+    Parameters(seed=1)
+    with pytest.raises(ValidationError):
+        Parameters(seed=-1)
+
+    # Test temperature
+    Parameters(temperature=1)
+    with pytest.raises(ValidationError):
+        Parameters(temperature=0)
+    with pytest.raises(ValidationError):
+        Parameters(temperature=-1)
+
+    # Test top_k
+    Parameters(top_k=1)
+    with pytest.raises(ValidationError):
+        Parameters(top_k=0)
+    with pytest.raises(ValidationError):
+        Parameters(top_k=-1)
+
+    # Test top_p
+    Parameters(top_p=0.5)
+    with pytest.raises(ValidationError):
+        Parameters(top_p=0)
+    with pytest.raises(ValidationError):
+        Parameters(top_p=-1)
+    with pytest.raises(ValidationError):
+        Parameters(top_p=1)
+
+    # Test truncate
+    Parameters(truncate=1)
+    with pytest.raises(ValidationError):
+        Parameters(truncate=0)
+    with pytest.raises(ValidationError):
+        Parameters(truncate=-1)
+
+    # Test typical_p
+    Parameters(typical_p=0.5)
+    with pytest.raises(ValidationError):
+        Parameters(typical_p=0)
+    with pytest.raises(ValidationError):
+        Parameters(typical_p=-1)
+    with pytest.raises(ValidationError):
+        Parameters(typical_p=1)
+
+
+def test_request_validation():
+    Request(inputs="test")
+
+    with pytest.raises(ValidationError):
+        Request(inputs="")
+
+    Request(inputs="test", stream=True)
+    Request(inputs="test", parameters=Parameters(best_of=2, do_sample=True))
+
+    with pytest.raises(ValidationError):
+        Request(
+            inputs="test", parameters=Parameters(best_of=2, do_sample=True), stream=True
+        )
diff --git a/clients/python/text_generation/__init__.py b/clients/python/text_generation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..46109833cc5388d2e2d68eae3e41171ed80ed95f
--- /dev/null
+++ b/clients/python/text_generation/__init__.py
@@ -0,0 +1,18 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+__version__ = "0.3.0"
+
+from text_generation.client import Client, AsyncClient
+from text_generation.inference_api import InferenceAPIClient, InferenceAPIAsyncClient
diff --git a/clients/python/text_generation/client.py b/clients/python/text_generation/client.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b8742fc4dcff671df9e13186f20cac28724b05b
--- /dev/null
+++ b/clients/python/text_generation/client.py
@@ -0,0 +1,481 @@
+import json
+import requests
+
+from aiohttp import ClientSession, ClientTimeout
+from pydantic import ValidationError
+from typing import Dict, Optional, List, AsyncIterator, Iterator
+
+from text_generation.types import (
+    StreamResponse,
+    Response,
+    Request,
+    Parameters,
+)
+from text_generation.errors import parse_error
+
+
+class Client:
+    """Client to make calls to a text-generation-inference instance
+
+     Example:
+
+     ```python
+     >>> from text_generation import Client
+
+     >>> client = Client("https://api-inference.huggingface.co/models/bigscience/bloomz")
+     >>> client.generate("Why is the sky blue?").generated_text
+     ' Rayleigh scattering'
+
+     >>> result = ""
+     >>> for response in client.generate_stream("Why is the sky blue?"):
+     >>>     if not response.token.special:
+     >>>         result += response.token.text
+     >>> result
+    ' Rayleigh scattering'
+     ```
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        headers: Optional[Dict[str, str]] = None,
+        cookies: Optional[Dict[str, str]] = None,
+        timeout: int = 10,
+    ):
+        """
+        Args:
+            base_url (`str`):
+                text-generation-inference instance base url
+            headers (`Optional[Dict[str, str]]`):
+                Additional headers
+            cookies (`Optional[Dict[str, str]]`):
+                Cookies to include in the requests
+            timeout (`int`):
+                Timeout in seconds
+        """
+        self.base_url = base_url
+        self.headers = headers
+        self.cookies = cookies
+        self.timeout = timeout
+
+    def generate(
+        self,
+        prompt: str,
+        do_sample: bool = False,
+        max_new_tokens: int = 20,
+        best_of: Optional[int] = None,
+        repetition_penalty: Optional[float] = None,
+        return_full_text: bool = False,
+        seed: Optional[int] = None,
+        stop_sequences: Optional[List[str]] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        truncate: Optional[int] = None,
+        typical_p: Optional[float] = None,
+        watermark: bool = False,
+    ) -> Response:
+        """
+        Given a prompt, generate the following text
+
+        Args:
+            prompt (`str`):
+                Input text
+            do_sample (`bool`):
+                Activate logits sampling
+            max_new_tokens (`int`):
+                Maximum number of generated tokens
+            best_of (`int`):
+                Generate best_of sequences and return the one if the highest token logprobs
+            repetition_penalty (`float`):
+                The parameter for repetition penalty. 1.0 means no penalty. See [this
+                paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            return_full_text (`bool`):
+                Whether to prepend the prompt to the generated text
+            seed (`int`):
+                Random sampling seed
+            stop_sequences (`List[str]`):
+                Stop generating tokens if a member of `stop_sequences` is generated
+            temperature (`float`):
+                The value used to module the logits distribution.
+            top_k (`int`):
+                The number of highest probability vocabulary tokens to keep for top-k-filtering.
+            top_p (`float`):
+                If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
+                higher are kept for generation.
+            truncate (`int`):
+                Truncate inputs tokens to the given size
+            typical_p (`float`):
+                Typical Decoding mass
+                See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
+            watermark (`bool`):
+                Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
+
+        Returns:
+            Response: generated response
+        """
+        # Validate parameters
+        parameters = Parameters(
+            best_of=best_of,
+            details=True,
+            do_sample=do_sample,
+            max_new_tokens=max_new_tokens,
+            repetition_penalty=repetition_penalty,
+            return_full_text=return_full_text,
+            seed=seed,
+            stop=stop_sequences if stop_sequences is not None else [],
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            truncate=truncate,
+            typical_p=typical_p,
+            watermark=watermark,
+        )
+        request = Request(inputs=prompt, stream=False, parameters=parameters)
+
+        resp = requests.post(
+            self.base_url,
+            json=request.dict(),
+            headers=self.headers,
+            cookies=self.cookies,
+            timeout=self.timeout,
+        )
+        payload = resp.json()
+        if resp.status_code != 200:
+            raise parse_error(resp.status_code, payload)
+        return Response(**payload[0])
+
+    def generate_stream(
+        self,
+        prompt: str,
+        do_sample: bool = False,
+        max_new_tokens: int = 20,
+        repetition_penalty: Optional[float] = None,
+        return_full_text: bool = False,
+        seed: Optional[int] = None,
+        stop_sequences: Optional[List[str]] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        truncate: Optional[int] = None,
+        typical_p: Optional[float] = None,
+        watermark: bool = False,
+    ) -> Iterator[StreamResponse]:
+        """
+        Given a prompt, generate the following stream of tokens
+
+        Args:
+            prompt (`str`):
+                Input text
+            do_sample (`bool`):
+                Activate logits sampling
+            max_new_tokens (`int`):
+                Maximum number of generated tokens
+            repetition_penalty (`float`):
+                The parameter for repetition penalty. 1.0 means no penalty. See [this
+                paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            return_full_text (`bool`):
+                Whether to prepend the prompt to the generated text
+            seed (`int`):
+                Random sampling seed
+            stop_sequences (`List[str]`):
+                Stop generating tokens if a member of `stop_sequences` is generated
+            temperature (`float`):
+                The value used to module the logits distribution.
+            top_k (`int`):
+                The number of highest probability vocabulary tokens to keep for top-k-filtering.
+            top_p (`float`):
+                If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
+                higher are kept for generation.
+            truncate (`int`):
+                Truncate inputs tokens to the given size
+            typical_p (`float`):
+                Typical Decoding mass
+                See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
+            watermark (`bool`):
+                Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
+
+        Returns:
+            Iterator[StreamResponse]: stream of generated tokens
+        """
+        # Validate parameters
+        parameters = Parameters(
+            best_of=None,
+            details=True,
+            do_sample=do_sample,
+            max_new_tokens=max_new_tokens,
+            repetition_penalty=repetition_penalty,
+            return_full_text=return_full_text,
+            seed=seed,
+            stop=stop_sequences if stop_sequences is not None else [],
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            truncate=truncate,
+            typical_p=typical_p,
+            watermark=watermark,
+        )
+        request = Request(inputs=prompt, stream=True, parameters=parameters)
+
+        resp = requests.post(
+            self.base_url,
+            json=request.dict(),
+            headers=self.headers,
+            cookies=self.cookies,
+            timeout=self.timeout,
+            stream=True,
+        )
+
+        if resp.status_code != 200:
+            raise parse_error(resp.status_code, resp.json())
+
+        # Parse ServerSentEvents
+        for byte_payload in resp.iter_lines():
+            # Skip line
+            if byte_payload == b"\n":
+                continue
+
+            payload = byte_payload.decode("utf-8")
+
+            # Event data
+            if payload.startswith("data:"):
+                # Decode payload
+                json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+                # Parse payload
+                try:
+                    response = StreamResponse(**json_payload)
+                except ValidationError:
+                    # If we failed to parse the payload, then it is an error payload
+                    raise parse_error(resp.status_code, json_payload)
+                yield response
+
+
+class AsyncClient:
+    """Asynchronous Client to make calls to a text-generation-inference instance
+
+     Example:
+
+     ```python
+     >>> from text_generation import AsyncClient
+
+     >>> client = AsyncClient("https://api-inference.huggingface.co/models/bigscience/bloomz")
+     >>> response = await client.generate("Why is the sky blue?")
+     >>> response.generated_text
+     ' Rayleigh scattering'
+
+     >>> result = ""
+     >>> async for response in client.generate_stream("Why is the sky blue?"):
+     >>>     if not response.token.special:
+     >>>         result += response.token.text
+     >>> result
+    ' Rayleigh scattering'
+     ```
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        headers: Optional[Dict[str, str]] = None,
+        cookies: Optional[Dict[str, str]] = None,
+        timeout: int = 10,
+    ):
+        """
+        Args:
+            base_url (`str`):
+                text-generation-inference instance base url
+            headers (`Optional[Dict[str, str]]`):
+                Additional headers
+            cookies (`Optional[Dict[str, str]]`):
+                Cookies to include in the requests
+            timeout (`int`):
+                Timeout in seconds
+        """
+        self.base_url = base_url
+        self.headers = headers
+        self.cookies = cookies
+        self.timeout = ClientTimeout(timeout * 60)
+
+    async def generate(
+        self,
+        prompt: str,
+        do_sample: bool = False,
+        max_new_tokens: int = 20,
+        best_of: Optional[int] = None,
+        repetition_penalty: Optional[float] = None,
+        return_full_text: bool = False,
+        seed: Optional[int] = None,
+        stop_sequences: Optional[List[str]] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        truncate: Optional[int] = None,
+        typical_p: Optional[float] = None,
+        watermark: bool = False,
+    ) -> Response:
+        """
+        Given a prompt, generate the following text asynchronously
+
+        Args:
+            prompt (`str`):
+                Input text
+            do_sample (`bool`):
+                Activate logits sampling
+            max_new_tokens (`int`):
+                Maximum number of generated tokens
+            best_of (`int`):
+                Generate best_of sequences and return the one if the highest token logprobs
+            repetition_penalty (`float`):
+                The parameter for repetition penalty. 1.0 means no penalty. See [this
+                paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            return_full_text (`bool`):
+                Whether to prepend the prompt to the generated text
+            seed (`int`):
+                Random sampling seed
+            stop_sequences (`List[str]`):
+                Stop generating tokens if a member of `stop_sequences` is generated
+            temperature (`float`):
+                The value used to module the logits distribution.
+            top_k (`int`):
+                The number of highest probability vocabulary tokens to keep for top-k-filtering.
+            top_p (`float`):
+                If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
+                higher are kept for generation.
+            truncate (`int`):
+                Truncate inputs tokens to the given size
+            typical_p (`float`):
+                Typical Decoding mass
+                See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
+            watermark (`bool`):
+                Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
+
+        Returns:
+            Response: generated response
+        """
+        # Validate parameters
+        parameters = Parameters(
+            best_of=best_of,
+            details=True,
+            do_sample=do_sample,
+            max_new_tokens=max_new_tokens,
+            repetition_penalty=repetition_penalty,
+            return_full_text=return_full_text,
+            seed=seed,
+            stop=stop_sequences if stop_sequences is not None else [],
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            truncate=truncate,
+            typical_p=typical_p,
+            watermark=watermark,
+        )
+        request = Request(inputs=prompt, stream=False, parameters=parameters)
+
+        async with ClientSession(
+            headers=self.headers, cookies=self.cookies, timeout=self.timeout
+        ) as session:
+            async with session.post(self.base_url, json=request.dict()) as resp:
+                payload = await resp.json()
+
+                if resp.status != 200:
+                    raise parse_error(resp.status, payload)
+                return Response(**payload[0])
+
+    async def generate_stream(
+        self,
+        prompt: str,
+        do_sample: bool = False,
+        max_new_tokens: int = 20,
+        repetition_penalty: Optional[float] = None,
+        return_full_text: bool = False,
+        seed: Optional[int] = None,
+        stop_sequences: Optional[List[str]] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        truncate: Optional[int] = None,
+        typical_p: Optional[float] = None,
+        watermark: bool = False,
+    ) -> AsyncIterator[StreamResponse]:
+        """
+        Given a prompt, generate the following stream of tokens asynchronously
+
+        Args:
+            prompt (`str`):
+                Input text
+            do_sample (`bool`):
+                Activate logits sampling
+            max_new_tokens (`int`):
+                Maximum number of generated tokens
+            repetition_penalty (`float`):
+                The parameter for repetition penalty. 1.0 means no penalty. See [this
+                paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            return_full_text (`bool`):
+                Whether to prepend the prompt to the generated text
+            seed (`int`):
+                Random sampling seed
+            stop_sequences (`List[str]`):
+                Stop generating tokens if a member of `stop_sequences` is generated
+            temperature (`float`):
+                The value used to module the logits distribution.
+            top_k (`int`):
+                The number of highest probability vocabulary tokens to keep for top-k-filtering.
+            top_p (`float`):
+                If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
+                higher are kept for generation.
+            truncate (`int`):
+                Truncate inputs tokens to the given size
+            typical_p (`float`):
+                Typical Decoding mass
+                See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
+            watermark (`bool`):
+                Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
+
+        Returns:
+            AsyncIterator[StreamResponse]: stream of generated tokens
+        """
+        # Validate parameters
+        parameters = Parameters(
+            best_of=None,
+            details=True,
+            do_sample=do_sample,
+            max_new_tokens=max_new_tokens,
+            repetition_penalty=repetition_penalty,
+            return_full_text=return_full_text,
+            seed=seed,
+            stop=stop_sequences if stop_sequences is not None else [],
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            truncate=truncate,
+            typical_p=typical_p,
+            watermark=watermark,
+        )
+        request = Request(inputs=prompt, stream=True, parameters=parameters)
+
+        async with ClientSession(
+            headers=self.headers, cookies=self.cookies, timeout=self.timeout
+        ) as session:
+            async with session.post(self.base_url, json=request.dict()) as resp:
+
+                if resp.status != 200:
+                    raise parse_error(resp.status, await resp.json())
+
+                # Parse ServerSentEvents
+                async for byte_payload in resp.content:
+                    # Skip line
+                    if byte_payload == b"\n":
+                        continue
+
+                    payload = byte_payload.decode("utf-8")
+
+                    # Event data
+                    if payload.startswith("data:"):
+                        # Decode payload
+                        json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+                        # Parse payload
+                        try:
+                            response = StreamResponse(**json_payload)
+                        except ValidationError:
+                            # If we failed to parse the payload, then it is an error payload
+                            raise parse_error(resp.status, json_payload)
+                        yield response
diff --git a/clients/python/text_generation/errors.py b/clients/python/text_generation/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbf0b761ae3a1c9df2fab28e7f59f338bc10f7a3
--- /dev/null
+++ b/clients/python/text_generation/errors.py
@@ -0,0 +1,106 @@
+from typing import Dict
+
+
+# Text Generation Inference Errors
+class ValidationError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class GenerationError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class OverloadedError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class IncompleteGenerationError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+# API Inference Errors
+class BadRequestError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class ShardNotReadyError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class ShardTimeoutError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class NotFoundError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class RateLimitExceededError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class NotSupportedError(Exception):
+    def __init__(self, model_id: str):
+        message = (
+            f"Model `{model_id}` is not available for inference with this client. \n"
+            "Use `huggingface_hub.inference_api.InferenceApi` instead."
+        )
+        super(NotSupportedError, self).__init__(message)
+
+
+# Unknown error
+class UnknownError(Exception):
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+def parse_error(status_code: int, payload: Dict[str, str]) -> Exception:
+    """
+    Parse error given an HTTP status code and a json payload
+
+    Args:
+        status_code (`int`):
+            HTTP status code
+        payload (`Dict[str, str]`):
+            Json payload
+
+    Returns:
+        Exception: parsed exception
+
+    """
+    # Try to parse a Text Generation Inference error
+    message = payload["error"]
+    if "error_type" in payload:
+        error_type = payload["error_type"]
+        if error_type == "generation":
+            return GenerationError(message)
+        if error_type == "incomplete_generation":
+            return IncompleteGenerationError(message)
+        if error_type == "overloaded":
+            return OverloadedError(message)
+        if error_type == "validation":
+            return ValidationError(message)
+
+    # Try to parse a APIInference error
+    if status_code == 400:
+        return BadRequestError(message)
+    if status_code == 403 or status_code == 424:
+        return ShardNotReadyError(message)
+    if status_code == 504:
+        return ShardTimeoutError(message)
+    if status_code == 404:
+        return NotFoundError(message)
+    if status_code == 429:
+        return RateLimitExceededError(message)
+
+    # Fallback to an unknown error
+    return UnknownError(message)
diff --git a/clients/python/text_generation/inference_api.py b/clients/python/text_generation/inference_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..93b0de8d42c81f2bf2b428239e71eda6f37b3c24
--- /dev/null
+++ b/clients/python/text_generation/inference_api.py
@@ -0,0 +1,168 @@
+import os
+import requests
+
+from typing import Dict, Optional, List
+from huggingface_hub.utils import build_hf_headers
+
+from text_generation import Client, AsyncClient, __version__
+from text_generation.types import DeployedModel
+from text_generation.errors import NotSupportedError, parse_error
+
+INFERENCE_ENDPOINT = os.environ.get(
+    "HF_INFERENCE_ENDPOINT", "https://api-inference.huggingface.co"
+)
+
+
+def deployed_models(headers: Optional[Dict] = None) -> List[DeployedModel]:
+    """
+    Get all currently deployed models with text-generation-inference-support
+
+    Returns:
+        List[DeployedModel]: list of all currently deployed models
+    """
+    resp = requests.get(
+        f"https://api-inference.huggingface.co/framework/text-generation-inference",
+        headers=headers,
+        timeout=5,
+    )
+
+    payload = resp.json()
+    if resp.status_code != 200:
+        raise parse_error(resp.status_code, payload)
+
+    models = [DeployedModel(**raw_deployed_model) for raw_deployed_model in payload]
+    return models
+
+
+def check_model_support(repo_id: str, headers: Optional[Dict] = None) -> bool:
+    """
+    Check if a given model is supported by text-generation-inference
+
+    Returns:
+        bool: whether the model is supported by this client
+    """
+    resp = requests.get(
+        f"https://api-inference.huggingface.co/status/{repo_id}",
+        headers=headers,
+        timeout=5,
+    )
+
+    payload = resp.json()
+    if resp.status_code != 200:
+        raise parse_error(resp.status_code, payload)
+
+    framework = payload["framework"]
+    supported = framework == "text-generation-inference"
+    return supported
+
+
+class InferenceAPIClient(Client):
+    """Client to make calls to the HuggingFace Inference API.
+
+     Only supports a subset of the available text-generation or text2text-generation models that are served using
+     text-generation-inference
+
+     Example:
+
+     ```python
+     >>> from text_generation import InferenceAPIClient
+
+     >>> client = InferenceAPIClient("bigscience/bloomz")
+     >>> client.generate("Why is the sky blue?").generated_text
+     ' Rayleigh scattering'
+
+     >>> result = ""
+     >>> for response in client.generate_stream("Why is the sky blue?"):
+     >>>     if not response.token.special:
+     >>>         result += response.token.text
+     >>> result
+    ' Rayleigh scattering'
+     ```
+    """
+
+    def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
+        """
+        Init headers and API information
+
+        Args:
+            repo_id (`str`):
+                Id of repository (e.g. `bigscience/bloom`).
+            token (`str`, `optional`):
+                The API token to use as HTTP bearer authorization. This is not
+                the authentication token. You can find the token in
+                https://huggingface.co/settings/token. Alternatively, you can
+                find both your organizations and personal API tokens using
+                `HfApi().whoami(token)`.
+            timeout (`int`):
+                Timeout in seconds
+        """
+
+        headers = build_hf_headers(
+            token=token, library_name="text-generation", library_version=__version__
+        )
+
+        # Text Generation Inference client only supports a subset of the available hub models
+        if not check_model_support(repo_id, headers):
+            raise NotSupportedError(repo_id)
+
+        base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
+
+        super(InferenceAPIClient, self).__init__(
+            base_url, headers=headers, timeout=timeout
+        )
+
+
+class InferenceAPIAsyncClient(AsyncClient):
+    """Aynschronous Client to make calls to the HuggingFace Inference API.
+
+     Only supports a subset of the available text-generation or text2text-generation models that are served using
+     text-generation-inference
+
+     Example:
+
+     ```python
+     >>> from text_generation import InferenceAPIAsyncClient
+
+     >>> client = InferenceAPIAsyncClient("bigscience/bloomz")
+     >>> response = await client.generate("Why is the sky blue?")
+     >>> response.generated_text
+     ' Rayleigh scattering'
+
+     >>> result = ""
+     >>> async for response in client.generate_stream("Why is the sky blue?"):
+     >>>     if not response.token.special:
+     >>>         result += response.token.text
+     >>> result
+    ' Rayleigh scattering'
+     ```
+    """
+
+    def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
+        """
+        Init headers and API information
+
+        Args:
+            repo_id (`str`):
+                Id of repository (e.g. `bigscience/bloom`).
+            token (`str`, `optional`):
+                The API token to use as HTTP bearer authorization. This is not
+                the authentication token. You can find the token in
+                https://huggingface.co/settings/token. Alternatively, you can
+                find both your organizations and personal API tokens using
+                `HfApi().whoami(token)`.
+            timeout (`int`):
+                Timeout in seconds
+        """
+        headers = build_hf_headers(
+            token=token, library_name="text-generation", library_version=__version__
+        )
+
+        # Text Generation Inference client only supports a subset of the available hub models
+        if not check_model_support(repo_id, headers):
+            raise NotSupportedError(repo_id)
+
+        base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
+
+        super(InferenceAPIAsyncClient, self).__init__(
+            base_url, headers=headers, timeout=timeout
+        )
diff --git a/clients/python/text_generation/types.py b/clients/python/text_generation/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3f9dcb5a6a0dfc6679dc0c1c3dfbcce32e0531c
--- /dev/null
+++ b/clients/python/text_generation/types.py
@@ -0,0 +1,231 @@
+from enum import Enum
+from pydantic import BaseModel, validator
+from typing import Optional, List
+
+from text_generation.errors import ValidationError
+
+
+class Parameters(BaseModel):
+    # Activate logits sampling
+    do_sample: bool = False
+    # Maximum number of generated tokens
+    max_new_tokens: int = 20
+    # The parameter for repetition penalty. 1.0 means no penalty.
+    # See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+    repetition_penalty: Optional[float] = None
+    # Whether to prepend the prompt to the generated text
+    return_full_text: bool = False
+    # Stop generating tokens if a member of `stop_sequences` is generated
+    stop: List[str] = []
+    # Random sampling seed
+    seed: Optional[int]
+    # The value used to module the logits distribution.
+    temperature: Optional[float]
+    # The number of highest probability vocabulary tokens to keep for top-k-filtering.
+    top_k: Optional[int]
+    # If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
+    # higher are kept for generation.
+    top_p: Optional[float]
+    # truncate inputs tokens to the given size
+    truncate: Optional[int]
+    # Typical Decoding mass
+    # See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
+    typical_p: Optional[float]
+    # Generate best_of sequences and return the one if the highest token logprobs
+    best_of: Optional[int]
+    # Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
+    watermark: bool = False
+    # Get generation details
+    details: bool = False
+
+    @validator("best_of")
+    def valid_best_of(cls, field_value, values):
+        if field_value is not None:
+            if field_value <= 0:
+                raise ValidationError("`best_of` must be strictly positive")
+            if field_value > 1 and values["seed"] is not None:
+                raise ValidationError("`seed` must not be set when `best_of` is > 1")
+            sampling = (
+                values["do_sample"]
+                | (values["temperature"] is not None)
+                | (values["top_k"] is not None)
+                | (values["top_p"] is not None)
+                | (values["typical_p"] is not None)
+            )
+            if field_value > 1 and not sampling:
+                raise ValidationError("you must use sampling when `best_of` is > 1")
+
+        return field_value
+
+    @validator("repetition_penalty")
+    def valid_repetition_penalty(cls, v):
+        if v is not None and v <= 0:
+            raise ValidationError("`repetition_penalty` must be strictly positive")
+        return v
+
+    @validator("seed")
+    def valid_seed(cls, v):
+        if v is not None and v < 0:
+            raise ValidationError("`seed` must be positive")
+        return v
+
+    @validator("temperature")
+    def valid_temp(cls, v):
+        if v is not None and v <= 0:
+            raise ValidationError("`temperature` must be strictly positive")
+        return v
+
+    @validator("top_k")
+    def valid_top_k(cls, v):
+        if v is not None and v <= 0:
+            raise ValidationError("`top_k` must be strictly positive")
+        return v
+
+    @validator("top_p")
+    def valid_top_p(cls, v):
+        if v is not None and (v <= 0 or v >= 1.0):
+            raise ValidationError("`top_p` must be > 0.0 and < 1.0")
+        return v
+
+    @validator("truncate")
+    def valid_truncate(cls, v):
+        if v is not None and v <= 0:
+            raise ValidationError("`truncate` must be strictly positive")
+        return v
+
+    @validator("typical_p")
+    def valid_typical_p(cls, v):
+        if v is not None and (v <= 0 or v >= 1.0):
+            raise ValidationError("`typical_p` must be > 0.0 and < 1.0")
+        return v
+
+
+class Request(BaseModel):
+    # Prompt
+    inputs: str
+    # Generation parameters
+    parameters: Optional[Parameters]
+    # Whether to stream output tokens
+    stream: bool = False
+
+    @validator("inputs")
+    def valid_input(cls, v):
+        if not v:
+            raise ValidationError("`inputs` cannot be empty")
+        return v
+
+    @validator("stream")
+    def valid_best_of_stream(cls, field_value, values):
+        parameters = values["parameters"]
+        if (
+            parameters is not None
+            and parameters.best_of is not None
+            and parameters.best_of > 1
+            and field_value
+        ):
+            raise ValidationError(
+                "`best_of` != 1 is not supported when `stream` == True"
+            )
+        return field_value
+
+
+# Prompt tokens
+class PrefillToken(BaseModel):
+    # Token ID from the model tokenizer
+    id: int
+    # Token text
+    text: str
+    # Logprob
+    # Optional since the logprob of the first token cannot be computed
+    logprob: Optional[float]
+
+
+# Generated tokens
+class Token(BaseModel):
+    # Token ID from the model tokenizer
+    id: int
+    # Token text
+    text: str
+    # Logprob
+    logprob: float
+    # Is the token a special token
+    # Can be used to ignore tokens when concatenating
+    special: bool
+
+
+# Generation finish reason
+class FinishReason(Enum):
+    # number of generated tokens == `max_new_tokens`
+    Length = "length"
+    # the model generated its end of sequence token
+    EndOfSequenceToken = "eos_token"
+    # the model generated a text included in `stop_sequences`
+    StopSequence = "stop_sequence"
+
+
+# Additional sequences when using the `best_of` parameter
+class BestOfSequence(BaseModel):
+    # Generated text
+    generated_text: str
+    # Generation finish reason
+    finish_reason: FinishReason
+    # Number of generated tokens
+    generated_tokens: int
+    # Sampling seed if sampling was activated
+    seed: Optional[int]
+    # Prompt tokens
+    prefill: List[PrefillToken]
+    # Generated tokens
+    tokens: List[Token]
+
+
+# `generate` details
+class Details(BaseModel):
+    # Generation finish reason
+    finish_reason: FinishReason
+    # Number of generated tokens
+    generated_tokens: int
+    # Sampling seed if sampling was activated
+    seed: Optional[int]
+    # Prompt tokens
+    prefill: List[PrefillToken]
+    # Generated tokens
+    tokens: List[Token]
+    # Additional sequences when using the `best_of` parameter
+    best_of_sequences: Optional[List[BestOfSequence]]
+
+
+# `generate` return value
+class Response(BaseModel):
+    # Generated text
+    generated_text: str
+    # Generation details
+    details: Details
+
+
+# `generate_stream` details
+class StreamDetails(BaseModel):
+    # Generation finish reason
+    finish_reason: FinishReason
+    # Number of generated tokens
+    generated_tokens: int
+    # Sampling seed if sampling was activated
+    seed: Optional[int]
+
+
+# `generate_stream` return value
+class StreamResponse(BaseModel):
+    # Generated token
+    token: Token
+    # Complete generated text
+    # Only available when the generation is finished
+    generated_text: Optional[str]
+    # Generation details
+    # Only available when the generation is finished
+    details: Optional[StreamDetails]
+
+
+# Inference API currently deployed model
+class DeployedModel(BaseModel):
+    model_id: str
+    sha: str
diff --git a/docs/index.html b/docs/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..16d143d808105c32edcd63934f27604b4d1f76f3
--- /dev/null
+++ b/docs/index.html
@@ -0,0 +1,30 @@
+<html>
+    <head>
+        <!-- Load the latest Swagger UI code and style from npm using unpkg.com -->
+        <script src="https://unpkg.com/swagger-ui-dist@3/swagger-ui-bundle.js"></script>
+        <link rel="stylesheet" type="text/css" href="https://unpkg.com/swagger-ui-dist@3/swagger-ui.css"/>
+        <title>Text Generation Inference API</title>
+    </head>
+    <body>
+        <div id="swagger-ui"></div> <!-- Div to hold the UI component -->
+        <script>
+            window.onload = function () {
+                // Begin Swagger UI call region
+                const ui = SwaggerUIBundle({
+                    url: "openapi.json", //Location of Open API spec in the repo
+                    dom_id: '#swagger-ui',
+                    deepLinking: true,
+                    supportedSubmitMethods: [],
+                    presets: [
+                        SwaggerUIBundle.presets.apis,
+                        SwaggerUIBundle.SwaggerUIStandalonePreset
+                    ],
+                    plugins: [
+                        SwaggerUIBundle.plugins.DownloadUrl
+                    ],
+                })
+                window.ui = ui
+            }
+        </script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/docs/openapi.json b/docs/openapi.json
new file mode 100644
index 0000000000000000000000000000000000000000..e7b7b140e3b4c6f87fe96c7fa49ba1c3bc703ef2
--- /dev/null
+++ b/docs/openapi.json
@@ -0,0 +1,730 @@
+{
+  "openapi": "3.0.3",
+  "info": {
+    "title": "Text Generation Inference",
+    "description": "Text Generation Webserver",
+    "contact": {
+      "name": "Olivier Dehaene"
+    },
+    "license": {
+      "name": "Apache 2.0",
+      "url": "https://www.apache.org/licenses/LICENSE-2.0"
+    },
+    "version": "0.6.0"
+  },
+  "paths": {
+    "/": {
+      "post": {
+        "tags": [
+          "Text Generation Inference"
+        ],
+        "summary": "Generate tokens if `stream == false` or a stream of token if `stream == true`",
+        "description": "Generate tokens if `stream == false` or a stream of token if `stream == true`",
+        "operationId": "compat_generate",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/CompatGenerateRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "See /generate or /generate_stream"
+          },
+          "422": {
+            "description": "Input validation error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Input validation error"
+                }
+              }
+            }
+          },
+          "424": {
+            "description": "Generation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Request failed during generation"
+                }
+              }
+            }
+          },
+          "429": {
+            "description": "Model is overloaded",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Model is overloaded"
+                }
+              }
+            }
+          },
+          "500": {
+            "description": "Incomplete generation",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Incomplete generation"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/generate": {
+      "post": {
+        "tags": [
+          "Text Generation Inference"
+        ],
+        "summary": "Generate tokens",
+        "description": "Generate tokens",
+        "operationId": "generate",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/GenerateRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Generated Text",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/GenerateResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Input validation error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Input validation error"
+                }
+              }
+            }
+          },
+          "424": {
+            "description": "Generation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Request failed during generation"
+                }
+              }
+            }
+          },
+          "429": {
+            "description": "Model is overloaded",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Model is overloaded"
+                }
+              }
+            }
+          },
+          "500": {
+            "description": "Incomplete generation",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Incomplete generation"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/generate_stream": {
+      "post": {
+        "tags": [
+          "Text Generation Inference"
+        ],
+        "summary": "Generate a stream of token using Server-Sent Events",
+        "description": "Generate a stream of token using Server-Sent Events",
+        "operationId": "generate_stream",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/GenerateRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Generated Text",
+            "content": {
+              "text/event-stream": {
+                "schema": {
+                  "$ref": "#/components/schemas/StreamResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Input validation error",
+            "content": {
+              "text/event-stream": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Input validation error"
+                }
+              }
+            }
+          },
+          "424": {
+            "description": "Generation Error",
+            "content": {
+              "text/event-stream": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Request failed during generation"
+                }
+              }
+            }
+          },
+          "429": {
+            "description": "Model is overloaded",
+            "content": {
+              "text/event-stream": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Model is overloaded"
+                }
+              }
+            }
+          },
+          "500": {
+            "description": "Incomplete generation",
+            "content": {
+              "text/event-stream": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Incomplete generation"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/info": {
+      "get": {
+        "tags": [
+          "Text Generation Inference"
+        ],
+        "summary": "Text Generation Inference endpoint info",
+        "description": "Text Generation Inference endpoint info",
+        "operationId": "get_model_info",
+        "responses": {
+          "200": {
+            "description": "Served model info",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/Info"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/metrics": {
+      "get": {
+        "tags": [
+          "Text Generation Inference"
+        ],
+        "summary": "Prometheus metrics scrape endpoint",
+        "description": "Prometheus metrics scrape endpoint",
+        "operationId": "metrics",
+        "responses": {
+          "200": {
+            "description": "Prometheus Metrics",
+            "content": {
+              "text/plain": {
+                "schema": {
+                  "type": "string"
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  },
+  "components": {
+    "schemas": {
+      "BestOfSequence": {
+        "type": "object",
+        "required": [
+          "generated_text",
+          "finish_reason",
+          "generated_tokens",
+          "prefill",
+          "tokens"
+        ],
+        "properties": {
+          "finish_reason": {
+            "$ref": "#/components/schemas/FinishReason"
+          },
+          "generated_text": {
+            "type": "string",
+            "example": "test"
+          },
+          "generated_tokens": {
+            "type": "integer",
+            "format": "int32",
+            "example": 1,
+            "minimum": 0.0
+          },
+          "prefill": {
+            "type": "array",
+            "items": {
+              "$ref": "#/components/schemas/PrefillToken"
+            }
+          },
+          "seed": {
+            "type": "integer",
+            "format": "int64",
+            "example": 42,
+            "nullable": true,
+            "minimum": 0.0
+          },
+          "tokens": {
+            "type": "array",
+            "items": {
+              "$ref": "#/components/schemas/Token"
+            }
+          }
+        }
+      },
+      "CompatGenerateRequest": {
+        "type": "object",
+        "required": [
+          "inputs"
+        ],
+        "properties": {
+          "inputs": {
+            "type": "string",
+            "example": "My name is Olivier and I"
+          },
+          "parameters": {
+            "$ref": "#/components/schemas/GenerateParameters"
+          },
+          "stream": {
+            "type": "boolean"
+          }
+        }
+      },
+      "Details": {
+        "type": "object",
+        "required": [
+          "finish_reason",
+          "generated_tokens",
+          "prefill",
+          "tokens"
+        ],
+        "properties": {
+          "best_of_sequences": {
+            "type": "array",
+            "items": {
+              "$ref": "#/components/schemas/BestOfSequence"
+            },
+            "nullable": true
+          },
+          "finish_reason": {
+            "$ref": "#/components/schemas/FinishReason"
+          },
+          "generated_tokens": {
+            "type": "integer",
+            "format": "int32",
+            "example": 1,
+            "minimum": 0.0
+          },
+          "prefill": {
+            "type": "array",
+            "items": {
+              "$ref": "#/components/schemas/PrefillToken"
+            }
+          },
+          "seed": {
+            "type": "integer",
+            "format": "int64",
+            "example": 42,
+            "nullable": true,
+            "minimum": 0.0
+          },
+          "tokens": {
+            "type": "array",
+            "items": {
+              "$ref": "#/components/schemas/Token"
+            }
+          }
+        }
+      },
+      "ErrorResponse": {
+        "type": "object",
+        "required": [
+          "error",
+          "error_type"
+        ],
+        "properties": {
+          "error": {
+            "type": "string"
+          },
+          "error_type": {
+            "type": "string"
+          }
+        }
+      },
+      "FinishReason": {
+        "type": "string",
+        "enum": [
+          "length",
+          "eos_token",
+          "stop_sequence"
+        ]
+      },
+      "GenerateParameters": {
+        "type": "object",
+        "properties": {
+          "best_of": {
+            "type": "integer",
+            "default": "null",
+            "example": 1,
+            "nullable": true,
+            "minimum": 0.0,
+            "exclusiveMinimum": 0.0
+          },
+          "details": {
+            "type": "boolean",
+            "default": "true"
+          },
+          "do_sample": {
+            "type": "boolean",
+            "default": "false",
+            "example": true
+          },
+          "max_new_tokens": {
+            "type": "integer",
+            "format": "int32",
+            "default": "20",
+            "minimum": 0.0,
+            "exclusiveMaximum": 512.0,
+            "exclusiveMinimum": 0.0
+          },
+          "repetition_penalty": {
+            "type": "number",
+            "format": "float",
+            "default": "null",
+            "example": 1.03,
+            "nullable": true,
+            "exclusiveMinimum": 0.0
+          },
+          "return_full_text": {
+            "type": "boolean",
+            "default": "null",
+            "example": false,
+            "nullable": true
+          },
+          "seed": {
+            "type": "integer",
+            "format": "int64",
+            "default": "null",
+            "example": "null",
+            "nullable": true,
+            "minimum": 0.0,
+            "exclusiveMinimum": 0.0
+          },
+          "stop": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            },
+            "example": [
+              "photographer"
+            ],
+            "maxItems": 4
+          },
+          "temperature": {
+            "type": "number",
+            "format": "float",
+            "default": "null",
+            "example": 0.5,
+            "nullable": true,
+            "exclusiveMinimum": 0.0
+          },
+          "top_k": {
+            "type": "integer",
+            "format": "int32",
+            "default": "null",
+            "example": 10,
+            "nullable": true,
+            "exclusiveMinimum": 0.0
+          },
+          "top_p": {
+            "type": "number",
+            "format": "float",
+            "default": "null",
+            "example": 0.95,
+            "nullable": true,
+            "maximum": 1.0,
+            "exclusiveMinimum": 0.0
+          },
+          "truncate": {
+            "type": "integer",
+            "default": "null",
+            "example": "null",
+            "nullable": true,
+            "minimum": 0.0
+          },
+          "typical_p": {
+            "type": "number",
+            "format": "float",
+            "default": "null",
+            "example": 0.95,
+            "nullable": true,
+            "maximum": 1.0,
+            "exclusiveMinimum": 0.0
+          },
+          "watermark": {
+            "type": "boolean",
+            "default": "false",
+            "example": true
+          }
+        }
+      },
+      "GenerateRequest": {
+        "type": "object",
+        "required": [
+          "inputs"
+        ],
+        "properties": {
+          "inputs": {
+            "type": "string",
+            "example": "My name is Olivier and I"
+          },
+          "parameters": {
+            "$ref": "#/components/schemas/GenerateParameters"
+          }
+        }
+      },
+      "GenerateResponse": {
+        "type": "object",
+        "required": [
+          "generated_text"
+        ],
+        "properties": {
+          "details": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/Details"
+              }
+            ],
+            "nullable": true
+          },
+          "generated_text": {
+            "type": "string",
+            "example": "test"
+          }
+        }
+      },
+      "Info": {
+        "type": "object",
+        "required": [
+          "model_id",
+          "version"
+        ],
+        "properties": {
+          "model_id": {
+            "type": "string",
+            "example": "bigscience/blomm-560m"
+          },
+          "model_pipeline_tag": {
+            "type": "string",
+            "example": "text-generation",
+            "nullable": true
+          },
+          "model_sha": {
+            "type": "string",
+            "example": "e985a63cdc139290c5f700ff1929f0b5942cced2",
+            "nullable": true
+          },
+          "sha": {
+            "type": "string",
+            "example": "null",
+            "nullable": true
+          },
+          "version": {
+            "type": "string",
+            "example": "0.5.0"
+          }
+        }
+      },
+      "PrefillToken": {
+        "type": "object",
+        "required": [
+          "id",
+          "text",
+          "logprob"
+        ],
+        "properties": {
+          "id": {
+            "type": "integer",
+            "format": "int32",
+            "example": 0,
+            "minimum": 0.0
+          },
+          "logprob": {
+            "type": "number",
+            "format": "float",
+            "example": -0.34,
+            "nullable": true
+          },
+          "text": {
+            "type": "string",
+            "example": "test"
+          }
+        }
+      },
+      "StreamDetails": {
+        "type": "object",
+        "required": [
+          "finish_reason",
+          "generated_tokens"
+        ],
+        "properties": {
+          "finish_reason": {
+            "$ref": "#/components/schemas/FinishReason"
+          },
+          "generated_tokens": {
+            "type": "integer",
+            "format": "int32",
+            "example": 1,
+            "minimum": 0.0
+          },
+          "seed": {
+            "type": "integer",
+            "format": "int64",
+            "example": 42,
+            "nullable": true,
+            "minimum": 0.0
+          }
+        }
+      },
+      "StreamResponse": {
+        "type": "object",
+        "required": [
+          "token"
+        ],
+        "properties": {
+          "details": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/StreamDetails"
+              }
+            ],
+            "nullable": true
+          },
+          "generated_text": {
+            "type": "string",
+            "default": "null",
+            "example": "test",
+            "nullable": true
+          },
+          "token": {
+            "$ref": "#/components/schemas/Token"
+          }
+        }
+      },
+      "Token": {
+        "type": "object",
+        "required": [
+          "id",
+          "text",
+          "logprob",
+          "special"
+        ],
+        "properties": {
+          "id": {
+            "type": "integer",
+            "format": "int32",
+            "example": 0,
+            "minimum": 0.0
+          },
+          "logprob": {
+            "type": "number",
+            "format": "float",
+            "example": -0.34,
+            "nullable": true
+          },
+          "special": {
+            "type": "boolean",
+            "example": "false"
+          },
+          "text": {
+            "type": "string",
+            "example": "test"
+          }
+        }
+      }
+    }
+  },
+  "tags": [
+    {
+      "name": "Text Generation Inference",
+      "description": "Hugging Face Text Generation Inference API"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/k6/load_test.js b/k6/load_test.js
new file mode 100644
index 0000000000000000000000000000000000000000..516b5666bfce0aca585670bc4f9de47b70d6581d
--- /dev/null
+++ b/k6/load_test.js
@@ -0,0 +1,98 @@
+import http from 'k6/http';
+import {check, sleep} from 'k6';
+
+export const options = {
+    stages: [
+        {duration: '1m', target: 50},
+        {duration: '2m', target: 100},
+        {duration: '1m', target: 0},
+    ],
+    hosts: {
+        'text-generation-inference.huggingface.co': '127.0.0.1:3000',
+    },
+};
+const SLEEP_DURATION = 1;
+
+function greedy_example(inputs, max_new_tokens, name) {
+    let body = JSON.stringify({
+        inputs: inputs,
+        parameters: {
+            max_new_tokens: max_new_tokens,
+            do_sample: false,
+        }
+    });
+    let params = {
+        headers: {
+            'Content-Type': 'application/json',
+        },
+        tags: {
+            name: name
+        }
+    };
+    return http.post('http://text-generation-inference.huggingface.co/generate', body, params);
+}
+
+function sample_example(inputs, max_new_tokens, name) {
+    let body = JSON.stringify({
+        inputs: inputs,
+        parameters: {
+            max_new_tokens: max_new_tokens,
+            do_sample: true,
+            top_p: 0.9,
+            seed: 0
+        }
+    });
+    let params = {
+        headers: {
+            'Content-Type': 'application/json',
+        },
+        tags: {
+            name: name
+        }
+    };
+    return http.post('http://text-generation-inference.huggingface.co/generate', body, params);
+}
+
+export default function () {
+    const response_1 = sample_example('A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is: We were traveling in Africa and we saw these very cute whatpus. To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:', 32, 'example-1');
+    check(response_1, {
+        'is status 200': (r) => r.status === 200,
+    });
+    sleep(SLEEP_DURATION);
+
+    const response_2 = sample_example("A poem about the beauty of science by Alfred Edgar Brittle\\nTitle: The Magic Craft\\nIn the old times", 50, "example-2");
+    check(response_2, {
+        'is status 200': (r) => r.status === 200,
+    });
+    sleep(SLEEP_DURATION);
+
+    const response_3 = greedy_example("استخراج العدد العاملي في لغة بايثون: ", 30, "example-3");
+    check(response_3, {
+        'is status 200': (r) => r.status === 200,
+    });
+    sleep(SLEEP_DURATION);
+
+    const response_4 = sample_example("Pour déguster un ortolan, il faut tout d'abord", 32, "example-4");
+    check(response_4, {
+        'is status 200': (r) => r.status === 200,
+    });
+    sleep(SLEEP_DURATION);
+
+    const response_5 = sample_example("Traduce español de España a español de Argentina\nEl coche es rojo - el auto es rojo\nEl ordenador es nuevo - la computadora es nueva\nel boligrafo es negro -", 16, "example-5");
+    check(response_5, {
+        'is status 200': (r) => r.status === 200,
+    });
+    sleep(SLEEP_DURATION);
+
+    const response_6 = sample_example("Question: If I put cheese into the fridge, will it melt?\nAnswer:", 32, "example-6");
+    check(response_6, {
+        'is status 200': (r) => r.status === 200,
+    });
+    sleep(SLEEP_DURATION);
+
+    const response_7 = greedy_example("Question: Where does the Greek Goddess Persephone spend half of the year when she is not with her mother?\nAnswer:", 24, "example-7");
+    check(response_7, {
+        'is status 200': (r) => r.status === 200,
+    });
+    sleep(SLEEP_DURATION);
+}
\ No newline at end of file
diff --git a/launcher/Cargo.toml b/launcher/Cargo.toml
new file mode 100644
index 0000000000000000000000000000000000000000..885dcd988f255eb02e0c4c5bd89b3cef449432aa
--- /dev/null
+++ b/launcher/Cargo.toml
@@ -0,0 +1,22 @@
+[package]
+name = "text-generation-launcher"
+version = "0.6.0"
+edition = "2021"
+authors = ["Olivier Dehaene"]
+description = "Text Generation Launcher"
+
+[dependencies]
+clap = { version = "4.1.4", features = ["derive", "env"] }
+ctrlc = { version = "3.2.5", features = ["termination"] }
+serde = { version = "1.0.152", features = ["derive"]  }
+serde_json = "1.0.93"
+subprocess = "0.2.9"
+tracing = "0.1.37"
+tracing-subscriber = { version = "0.3.16", features = ["json"] }
+
+[dev-dependencies]
+float_eq = "1.0.1"
+reqwest = { version = "0.11.14", features = ["blocking", "json"] }
+
+[build-dependencies]
+vergen = { version = "8.0.0", features = ["build", "cargo", "git", "gitcl", "rustc", "si"] }
diff --git a/launcher/build.rs b/launcher/build.rs
new file mode 100644
index 0000000000000000000000000000000000000000..71d2c0c597921926d535f731704cda52c990f839
--- /dev/null
+++ b/launcher/build.rs
@@ -0,0 +1,29 @@
+use std::error::Error;
+use vergen::EmitBuilder;
+
+fn main() -> Result<(), Box<dyn Error>> {
+    // Emit cargo and rustc compile time values
+    EmitBuilder::builder().all_cargo().all_rustc().emit()?;
+
+    // Try to get the git sha from the local git repository
+    if EmitBuilder::builder()
+        .fail_on_error()
+        .git_sha(false)
+        .emit()
+        .is_err()
+    {
+        // Unable to get the git sha
+        if let Ok(sha) = std::env::var("GIT_SHA") {
+            // Set it from an env var
+            println!("cargo:rustc-env=VERGEN_GIT_SHA={sha}");
+        }
+    }
+
+    // Set docker label if present
+    if let Ok(label) = std::env::var("DOCKER_LABEL") {
+        // Set it from an env var
+        println!("cargo:rustc-env=DOCKER_LABEL={label}");
+    }
+
+    Ok(())
+}
diff --git a/launcher/src/env_runtime.rs b/launcher/src/env_runtime.rs
new file mode 100644
index 0000000000000000000000000000000000000000..9dbc83f790791c6c08067dcc6e3b6b416ca88a26
--- /dev/null
+++ b/launcher/src/env_runtime.rs
@@ -0,0 +1,45 @@
+use std::fmt;
+use std::process::Command;
+
+pub(crate) struct Env {
+    cargo_target: &'static str,
+    cargo_version: &'static str,
+    git_sha: &'static str,
+    docker_label: &'static str,
+    nvidia_env: String,
+}
+
+impl Env {
+    pub fn new() -> Self {
+        let nvidia_env = nvidia_smi();
+
+        Self {
+            nvidia_env: nvidia_env.unwrap_or("N/A".to_string()),
+            cargo_target: env!("VERGEN_CARGO_TARGET_TRIPLE"),
+            cargo_version: env!("VERGEN_RUSTC_SEMVER"),
+            git_sha: option_env!("VERGEN_GIT_SHA").unwrap_or("N/A"),
+            docker_label: option_env!("DOCKER_LABEL").unwrap_or("N/A"),
+        }
+    }
+}
+
+impl fmt::Display for Env {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        writeln!(f, "Runtime environment:")?;
+
+        writeln!(f, "Target: {}", self.cargo_target)?;
+        writeln!(f, "Cargo version: {}", self.cargo_version)?;
+        writeln!(f, "Commit sha: {}", self.git_sha)?;
+        writeln!(f, "Docker label: {}", self.docker_label)?;
+        write!(f, "nvidia-smi:\n{}", self.nvidia_env)?;
+
+        Ok(())
+    }
+}
+
+fn nvidia_smi() -> Option<String> {
+    let output = Command::new("nvidia-smi").output().ok()?;
+    let nvidia_smi = String::from_utf8(output.stdout).ok()?;
+    let output = nvidia_smi.replace('\n', "\n   ");
+    Some(output.trim().to_string())
+}
diff --git a/launcher/src/main.rs b/launcher/src/main.rs
new file mode 100644
index 0000000000000000000000000000000000000000..736aa5a7fb34ec72585fa765eb97870e7ac56462
--- /dev/null
+++ b/launcher/src/main.rs
@@ -0,0 +1,925 @@
+use clap::Parser;
+use serde::Deserialize;
+use std::env;
+use std::ffi::OsString;
+use std::io::{BufRead, BufReader, Read};
+use std::path::Path;
+use std::sync::atomic::{AtomicBool, Ordering};
+use std::sync::mpsc::TryRecvError;
+use std::sync::Arc;
+use std::sync::{mpsc, Mutex};
+use std::thread;
+use std::thread::sleep;
+use std::time::{Duration, Instant};
+use std::{fs, io};
+use subprocess::{ExitStatus, Popen, PopenConfig, PopenError, Redirection};
+
+mod env_runtime;
+
+/// App Configuration
+#[derive(Parser, Debug)]
+#[clap(author, version, about, long_about = None)]
+struct Args {
+    /// The name of the model to load.
+    /// Can be a MODEL_ID as listed on <https://hf.co/models> like
+    /// `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`.
+    /// Or it can be a local directory containing the necessary files
+    /// as saved by `save_pretrained(...)` methods of transformers
+    #[clap(default_value = "bigscience/bloom-560m", long, env)]
+    model_id: String,
+
+    /// The actual revision of the model if you're referring to a model
+    /// on the hub. You can use a specific commit id or a branch like `refs/pr/2`.
+    #[clap(long, env)]
+    revision: Option<String>,
+
+    /// Wether to shard or not the model across multiple GPUs
+    /// By default text-generation-inference will use all available GPUs to run
+    /// the model. Setting it to `false` deactivates `num_shard`.
+    #[clap(long, env)]
+    sharded: Option<bool>,
+
+    /// The number of shards to use if you don't want to use all GPUs on a given machine.
+    /// You can use `CUDA_VISIBLE_DEVICE=0,1 text-generation-launcher... --num_shard 2`
+    /// and `CUDA_VISIBLE_DEVICE=2,3 text-generation-launcher... --num_shard 2` to
+    /// launch 2 copies with 2 shard each on a given machine with 4 GPUs for instance.
+    #[clap(long, env)]
+    num_shard: Option<usize>,
+
+    /// Wether you want the model to be quantized or not. This will use bitsandbytes for
+    /// quantization on the fly.
+    #[clap(long, env)]
+    quantize: bool,
+
+    /// The maximum amount of concurrent requests for this particular deployment.
+    /// Having a low limit will refuse clients requests instead of having them
+    /// wait for too long and is usually good to handle backpressure correctly.
+    #[clap(default_value = "128", long, env)]
+    max_concurrent_requests: usize,
+
+    /// This is the maximum allowed value for clients to set `best_of`.
+    /// Best of makes `n` generations at the same time, and return the best
+    /// in terms of overall log probability over the entire generated sequence
+    #[clap(default_value = "2", long, env)]
+    max_best_of: usize,
+
+    /// This is the maximum allowed value for clients to set `stop_sequences`.
+    /// Stop sequences are used to allow the model to stop on more than just
+    /// the EOS token, and enable more complex "prompting" where users can preprompt
+    /// the model in a specific way and define their "own" stop token aligned with
+    /// their prompt.
+    #[clap(default_value = "4", long, env)]
+    max_stop_sequences: usize,
+
+    /// This is the maximum allowed input length (expressed in number of tokens)
+    /// for users. The larger this value, the longer prompt users can send which
+    /// can impact the overall memory required to handle the load.
+    /// Please note that some models have a finite range of sequence they can handle.
+    #[clap(default_value = "1000", long, env)]
+    max_input_length: usize,
+
+    /// This is the most important value to set as it defines the "memory budget"
+    /// of running clients requests.
+    /// Clients will send input sequences and ask to generate `max_new_tokens`
+    /// on top. with a value of `1512` users can send either a prompt of
+    /// `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for
+    /// `1511` max_new_tokens.
+    /// The larger this value, the larger amount each request will be in your RAM
+    /// and the less effective batching can be.
+    #[clap(default_value = "1512", long, env)]
+    max_total_tokens: usize,
+
+    /// The maximum allowed batch size during dynamic batching.
+    /// Using `max_batch_total_tokens` should be favored in general
+    /// as it's a finer way to control RAM usage.
+    #[clap(long, env)]
+    max_batch_size: Option<usize>,
+
+    /// This represents the ratio of waiting queries vs running queries where
+    /// you want to start considering pausing the running queries to include the waiting
+    /// ones into the same batch.
+    /// `waiting_served_ratio=1.2` Means when 12 queries are waiting and there's
+    /// only 10 queries left in the current batch we check if we can fit those 12
+    /// waiting queries into the batching strategy, and if yes, then batching happens
+    /// delaying the 10 running queries by a `prefill` run.
+    ///
+    /// This setting is only applied if there is room in the batch
+    /// as defined by `max_batch_total_tokens`.
+    #[clap(default_value = "1.2", long, env)]
+    waiting_served_ratio: f32,
+
+    /// **IMPORTANT** This is one critical control to allow maximum usage
+    /// of the available hardware.
+    ///
+    /// This represents the total amount of potential tokens within a batch.
+    /// When using padding (not recommended) this would be equivalent of
+    /// `batch_size` * `max_total_tokens`.
+    ///
+    /// However in the non-padded (flash attention) version this can be much finer.
+    ///
+    /// For `max_batch_total_tokens=1000`, you could fit `10` queries of `total_tokens=100`
+    /// or a single query of `1000` tokens.
+    ///
+    /// So you don't have to control that finely
+    /// `max_batch_size` or `max_total_tokens`. In fact you could mostly relax them if you
+    /// want maximum flexibility. However, for your users if they are asking for the full amount of
+    /// total tokens, they are likely to wait for a very long time to get a spot
+    /// in the batch (since they are going to be alone) so setting `max_batch_size`
+    /// and `max_total_tokens` can still be useful to prevent those long waiting times.
+    ///
+    /// Overall this number should be the largest possible amount that fits the
+    /// remaining memory (after the model is loaded). Since the actual memory overhead
+    /// depends on other parameters like if you're using quantization, flash attention
+    /// or the model implementation, text-generation-inference cannot infer this number
+    /// automatically.
+    #[clap(default_value = "32000", long, env)]
+    max_batch_total_tokens: u32,
+
+    /// This setting defines how many tokens can be passed before forcing the waiting
+    /// queries to be put on the batch (if the size of the batch allows for it).
+    /// New queries require 1 `prefill` forward, which is different from `decode`
+    /// and therefore you need to pause the running batch in order to run `prefill`
+    /// to create the correct values for the waiting queries to be able to join the batch.
+    ///
+    /// With a value too small, queries will always "steal" the compute to run `prefill`
+    /// and running queries will be delayed by a lot.
+    ///
+    /// With a value too big, waiting queries could wait for a very long time
+    /// before being allowed a slot in the running batch. If your server is busy
+    /// that means that requests that could run in ~2s on an empty server could
+    /// end up running in ~20s because the query had to wait for 18s.
+    ///
+    /// This number is expressed in number of tokens to make it a bit more
+    /// "model" agnostic, but what should really matter is the overall latency
+    /// for end users.
+    #[clap(default_value = "20", long, env)]
+    max_waiting_tokens: usize,
+    #[clap(default_value = "3000", long, short, env)]
+
+    /// The port to listen on.
+    port: u16,
+
+    /// The name of the socket for gRPC communication between the webserver
+    /// and the shards.
+    #[clap(default_value = "/tmp/text-generation-server", long, env)]
+    shard_uds_path: String,
+
+    /// The address the master shard will listen on. (setting used by torch distributed)
+    #[clap(default_value = "localhost", long, env)]
+    master_addr: String,
+
+    /// The address the master port will listen on. (setting used by torch distributed)
+    #[clap(default_value = "29500", long, env)]
+    master_port: usize,
+
+    /// The location of the huggingface hub cache.
+    /// Used to override the location if you want to provide a mounted disk for instance
+    #[clap(long, env)]
+    huggingface_hub_cache: Option<String>,
+
+    /// The location of the huggingface hub cache.
+    /// Used to override the location if you want to provide a mounted disk for instance
+    #[clap(long, env)]
+    weights_cache_override: Option<String>,
+
+    /// For some models (like bloom), text-generation-inference implemented custom
+    /// cuda kernels to speed up inference. Those kernels were only tested on A100.
+    /// Use this flag to disable them if you're running on different hardware and
+    /// encounter issues.
+    #[clap(long, env)]
+    disable_custom_kernels: bool,
+
+    /// Outputs the logs in JSON format (useful for telemetry)
+    #[clap(long, env)]
+    json_output: bool,
+
+    #[clap(long, env)]
+    otlp_endpoint: Option<String>,
+
+    #[clap(long, env)]
+    cors_allow_origin: Vec<String>,
+    #[clap(long, env)]
+    watermark_gamma: Option<f32>,
+    #[clap(long, env)]
+    watermark_delta: Option<f32>,
+
+    /// Display a lot of information about your runtime environment
+    #[clap(long, short, action)]
+    env: bool,
+}
+
+#[derive(Debug)]
+enum ShardStatus {
+    Ready,
+    Failed((usize, String)),
+}
+
+#[allow(clippy::too_many_arguments)]
+fn shard_manager(
+    model_id: String,
+    revision: Option<String>,
+    quantize: bool,
+    uds_path: String,
+    rank: usize,
+    world_size: usize,
+    master_addr: String,
+    master_port: usize,
+    huggingface_hub_cache: Option<String>,
+    weights_cache_override: Option<String>,
+    disable_custom_kernels: bool,
+    watermark_gamma: Option<f32>,
+    watermark_delta: Option<f32>,
+    otlp_endpoint: Option<String>,
+    status_sender: mpsc::Sender<ShardStatus>,
+    shutdown: Arc<Mutex<bool>>,
+    _shutdown_sender: mpsc::Sender<()>,
+) {
+    // Get UDS path
+    let uds_string = format!("{uds_path}-{rank}");
+    let uds = Path::new(&uds_string);
+    // Clean previous runs
+    fs::remove_file(uds).unwrap_or_default();
+
+    // Process args
+    let mut shard_argv = vec![
+        "text-generation-server".to_string(),
+        "serve".to_string(),
+        model_id,
+        "--uds-path".to_string(),
+        uds_path,
+        "--logger-level".to_string(),
+        "INFO".to_string(),
+        "--json-output".to_string(),
+    ];
+
+    // Activate tensor parallelism
+    if world_size > 1 {
+        shard_argv.push("--sharded".to_string());
+    }
+
+    if quantize {
+        shard_argv.push("--quantize".to_string())
+    }
+
+    // Model optional revision
+    if let Some(revision) = revision {
+        shard_argv.push("--revision".to_string());
+        shard_argv.push(revision)
+    }
+
+    // OpenTelemetry
+    if let Some(otlp_endpoint) = otlp_endpoint {
+        shard_argv.push("--otlp-endpoint".to_string());
+        shard_argv.push(otlp_endpoint);
+    }
+
+    // Copy current process env
+    let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
+
+    // Torch Distributed Env vars
+    env.push(("RANK".into(), rank.to_string().into()));
+    env.push(("WORLD_SIZE".into(), world_size.to_string().into()));
+    env.push(("MASTER_ADDR".into(), master_addr.into()));
+    env.push(("MASTER_PORT".into(), master_port.to_string().into()));
+    env.push(("NCCL_ASYNC_ERROR_HANDLING".into(), "1".into()));
+
+    // Safetensors load fast
+    env.push(("SAFETENSORS_FAST_GPU".into(), "1".into()));
+
+    // Enable hf transfer for insane download speeds
+    let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
+    env.push((
+        "HF_HUB_ENABLE_HF_TRANSFER".into(),
+        enable_hf_transfer.into(),
+    ));
+
+    // Parse Inference API token
+    if let Ok(api_token) = env::var("HF_API_TOKEN") {
+        env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
+    };
+
+    // If huggingface_hub_cache is some, pass it to the shard
+    // Useful when running inside a docker container
+    if let Some(huggingface_hub_cache) = huggingface_hub_cache {
+        env.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
+    };
+
+    // If weights_cache_override is some, pass it to the shard
+    // Useful when running inside a HuggingFace Inference Endpoint
+    if let Some(weights_cache_override) = weights_cache_override {
+        env.push((
+            "WEIGHTS_CACHE_OVERRIDE".into(),
+            weights_cache_override.into(),
+        ));
+    };
+
+    // If disable_custom_kernels is true, pass it to the shard as an env var
+    if disable_custom_kernels {
+        env.push(("DISABLE_CUSTOM_KERNELS".into(), "True".into()))
+    }
+
+    // Watermark Gamma
+    if let Some(watermark_gamma) = watermark_gamma {
+        env.push(("WATERMARK_GAMMA".into(), watermark_gamma.to_string().into()))
+    }
+
+    // Watermark Delta
+    if let Some(watermark_delta) = watermark_delta {
+        env.push(("WATERMARK_DELTA".into(), watermark_delta.to_string().into()))
+    }
+
+    // Start process
+    tracing::info!("Starting shard {rank}");
+    let mut p = match Popen::create(
+        &shard_argv,
+        PopenConfig {
+            stdout: Redirection::Pipe,
+            stderr: Redirection::Pipe,
+            // Needed for the shutdown procedure
+            setpgid: true,
+            // NCCL env vars
+            env: Some(env),
+            ..Default::default()
+        },
+    ) {
+        Ok(p) => p,
+        Err(err) => {
+            if let PopenError::IoError(ref err) = err {
+                if err.kind() == io::ErrorKind::NotFound {
+                    tracing::error!("text-generation-server not found in PATH");
+                    tracing::error!("Please install it with `make install-server`")
+                }
+            }
+            status_sender
+                .send(ShardStatus::Failed((rank, err.to_string())))
+                .unwrap();
+            return;
+        }
+    };
+
+    // Redirect STDOUT to the console
+    let shard_stdout = p.stdout.take().unwrap();
+
+    thread::spawn(move || {
+        // Enter shard-manager tracing span
+        let stdout = BufReader::new(shard_stdout);
+        let _span = tracing::span!(tracing::Level::INFO, "shard-manager", rank = rank).entered();
+        for line in stdout.lines() {
+            // Parse loguru logs
+            if let Ok(log) = serde_json::from_str::<PythonLogMessage>(&line.unwrap()) {
+                log.trace();
+            }
+        }
+    });
+
+    let mut ready = false;
+    let start_time = Instant::now();
+    let mut wait_time = Instant::now();
+    loop {
+        // Process exited
+        if p.poll().is_some() {
+            let mut err = String::new();
+            p.stderr.take().unwrap().read_to_string(&mut err).unwrap();
+            status_sender
+                .send(ShardStatus::Failed((rank, err)))
+                .unwrap();
+            return;
+        }
+
+        // We received a shutdown signal
+        if *shutdown.lock().unwrap() {
+            p.terminate().unwrap();
+            let _ = p.wait_timeout(Duration::from_secs(90));
+            tracing::info!("Shard {rank} terminated");
+            return;
+        }
+
+        // Shard is ready
+        if uds.exists() && !ready {
+            tracing::info!("Shard {rank} ready in {:?}", start_time.elapsed());
+            status_sender.send(ShardStatus::Ready).unwrap();
+            ready = true;
+        } else if !ready && wait_time.elapsed() > Duration::from_secs(10) {
+            tracing::info!("Waiting for shard {rank} to be ready...");
+            wait_time = Instant::now();
+        }
+        sleep(Duration::from_millis(100));
+    }
+}
+
+fn shutdown_shards(shutdown: Arc<Mutex<bool>>, shutdown_receiver: &mpsc::Receiver<()>) {
+    tracing::info!("Shutting down shards");
+    // Update shutdown value to true
+    // This will be picked up by the shard manager
+    {
+        let mut shutdown = shutdown.lock().unwrap();
+        *shutdown = true;
+    }
+
+    // Wait for shards to shutdown
+    // This will block till all shutdown_sender are dropped
+    let _ = shutdown_receiver.recv();
+}
+
+fn num_cuda_devices() -> Option<usize> {
+    if let Ok(cuda_visible_devices) = env::var("CUDA_VISIBLE_DEVICES") {
+        let n_devices = cuda_visible_devices.split(',').count();
+        return Some(n_devices);
+    }
+    None
+}
+
+#[derive(Deserialize)]
+#[serde(rename_all = "UPPERCASE")]
+enum PythonLogLevelEnum {
+    Trace,
+    Debug,
+    Info,
+    Success,
+    Warning,
+    Error,
+    Critical,
+}
+
+#[derive(Deserialize)]
+struct PythonLogLevel {
+    name: PythonLogLevelEnum,
+}
+
+#[derive(Deserialize)]
+struct PythonLogRecord {
+    level: PythonLogLevel,
+}
+
+#[derive(Deserialize)]
+struct PythonLogMessage {
+    text: String,
+    record: PythonLogRecord,
+}
+
+impl PythonLogMessage {
+    fn trace(&self) {
+        match self.record.level.name {
+            PythonLogLevelEnum::Trace => tracing::trace!("{}", self.text),
+            PythonLogLevelEnum::Debug => tracing::debug!("{}", self.text),
+            PythonLogLevelEnum::Info => tracing::info!("{}", self.text),
+            PythonLogLevelEnum::Success => tracing::info!("{}", self.text),
+            PythonLogLevelEnum::Warning => tracing::warn!("{}", self.text),
+            PythonLogLevelEnum::Error => tracing::error!("{}", self.text),
+            PythonLogLevelEnum::Critical => tracing::error!("{}", self.text),
+        }
+    }
+}
+
+fn find_num_shards(sharded: Option<bool>, num_shard: Option<usize>) -> usize {
+    // get the number of shards given `sharded` and `num_shard`
+    let num_shard = match (sharded, num_shard) {
+        (Some(true), None) => {
+            // try to default to the number of available GPUs
+            tracing::info!("Parsing num_shard from CUDA_VISIBLE_DEVICES");
+            let n_devices =
+                num_cuda_devices().expect("--num-shard and CUDA_VISIBLE_DEVICES are not set");
+            if n_devices <= 1 {
+                panic!("`sharded` is true but only found {n_devices} CUDA devices");
+            }
+            n_devices
+        }
+        (Some(true), Some(num_shard)) => {
+            // we can't have only one shard while sharded
+            if num_shard <= 1 {
+                panic!("`sharded` is true but `num_shard` <= 1");
+            }
+            num_shard
+        }
+        (Some(false), Some(num_shard)) => num_shard,
+        (Some(false), None) => 1,
+        (None, None) => num_cuda_devices().unwrap_or(1),
+        (None, Some(num_shard)) => num_shard,
+    };
+    if num_shard < 1 {
+        panic!("`num_shard` cannot be < 1");
+    }
+    num_shard
+}
+
+#[derive(Debug)]
+enum LauncherError {
+    DownloadError,
+    ShardCannotStart,
+    ShardDisconnected,
+    ShardFailed,
+    WebserverFailed,
+    WebserverCannotStart,
+}
+
+fn download_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), LauncherError> {
+    let mut download_argv = vec![
+        "text-generation-server".to_string(),
+        "download-weights".to_string(),
+        args.model_id.to_string(),
+        "--extension".to_string(),
+        ".safetensors".to_string(),
+        "--logger-level".to_string(),
+        "INFO".to_string(),
+        "--json-output".to_string(),
+    ];
+
+    // Model optional revision
+    if let Some(revision) = &args.revision {
+        download_argv.push("--revision".to_string());
+        download_argv.push(revision.to_string())
+    }
+
+    // Copy current process env
+    let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
+
+    // If huggingface_hub_cache is set, pass it to the shard
+    // Useful when running inside a docker container
+    if let Some(ref huggingface_hub_cache) = args.huggingface_hub_cache {
+        env.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
+    };
+
+    // Enable hf transfer for insane download speeds
+    let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
+    env.push((
+        "HF_HUB_ENABLE_HF_TRANSFER".into(),
+        enable_hf_transfer.into(),
+    ));
+
+    // Parse Inference API token
+    if let Ok(api_token) = env::var("HF_API_TOKEN") {
+        env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
+    };
+
+    // Start process
+    tracing::info!("Starting download process.");
+    let mut download_process = match Popen::create(
+        &download_argv,
+        PopenConfig {
+            stdout: Redirection::Pipe,
+            stderr: Redirection::Pipe,
+            // Needed for the shutdown procedure
+            setpgid: true,
+            env: Some(env),
+            ..Default::default()
+        },
+    ) {
+        Ok(p) => p,
+        Err(err) => {
+            if let PopenError::IoError(ref err) = err {
+                if err.kind() == io::ErrorKind::NotFound {
+                    tracing::error!("text-generation-server not found in PATH");
+                    tracing::error!("Please install it with `make install-server`")
+                }
+            }
+            return Err(LauncherError::DownloadError);
+        }
+    };
+
+    // Redirect STDOUT to the console
+    let download_stdout = download_process.stdout.take().unwrap();
+    thread::spawn(move || {
+        // Enter download tracing span
+        let stdout = BufReader::new(download_stdout);
+        let _span = tracing::span!(tracing::Level::INFO, "download").entered();
+        for line in stdout.lines() {
+            // Parse loguru logs
+            if let Ok(log) = serde_json::from_str::<PythonLogMessage>(&line.unwrap()) {
+                log.trace();
+            }
+        }
+    });
+
+    loop {
+        if let Some(status) = download_process.poll() {
+            match status {
+                ExitStatus::Exited(exit_code) => {
+                    if exit_code == 0 {
+                        tracing::info!("Successfully downloaded weights.");
+                        break;
+                    } else {
+                        let mut err = String::new();
+                        download_process
+                            .stderr
+                            .take()
+                            .unwrap()
+                            .read_to_string(&mut err)
+                            .unwrap();
+                        tracing::error!("Download encountered an error: {err}");
+                        return Err(LauncherError::DownloadError);
+                    }
+                }
+                _ => {
+                    tracing::error!("Download process exited with an unknown status.");
+                    return Err(LauncherError::DownloadError);
+                }
+            }
+        }
+        if !running.load(Ordering::SeqCst) {
+            download_process.terminate().unwrap();
+            tracing::info!("Waiting for download process to gracefully shutdown");
+            download_process
+                .wait_timeout(Duration::from_secs(90))
+                .unwrap();
+            tracing::info!("Download process terminated");
+            return Ok(());
+        }
+        sleep(Duration::from_millis(100));
+    }
+    Ok(())
+}
+
+#[allow(clippy::too_many_arguments)]
+fn spawn_shards(
+    num_shard: usize,
+    args: &Args,
+    shutdown: Arc<Mutex<bool>>,
+    shutdown_receiver: &mpsc::Receiver<()>,
+    shutdown_sender: mpsc::Sender<()>,
+    status_receiver: &mpsc::Receiver<ShardStatus>,
+    status_sender: mpsc::Sender<ShardStatus>,
+    running: Arc<AtomicBool>,
+) -> Result<(), LauncherError> {
+    // Start shard processes
+    for rank in 0..num_shard {
+        let model_id = args.model_id.clone();
+        let revision = args.revision.clone();
+        let uds_path = args.shard_uds_path.clone();
+        let master_addr = args.master_addr.clone();
+        let huggingface_hub_cache = args.huggingface_hub_cache.clone();
+        let weights_cache_override = args.weights_cache_override.clone();
+        let status_sender = status_sender.clone();
+        let shutdown = shutdown.clone();
+        let shutdown_sender = shutdown_sender.clone();
+        let otlp_endpoint = args.otlp_endpoint.clone();
+        let quantize = args.quantize;
+        let master_port = args.master_port;
+        let disable_custom_kernels = args.disable_custom_kernels;
+        let watermark_gamma = args.watermark_gamma;
+        let watermark_delta = args.watermark_delta;
+        thread::spawn(move || {
+            shard_manager(
+                model_id,
+                revision,
+                quantize,
+                uds_path,
+                rank,
+                num_shard,
+                master_addr,
+                master_port,
+                huggingface_hub_cache,
+                weights_cache_override,
+                disable_custom_kernels,
+                watermark_gamma,
+                watermark_delta,
+                otlp_endpoint,
+                status_sender,
+                shutdown,
+                shutdown_sender,
+            )
+        });
+    }
+    drop(shutdown_sender);
+
+    // Wait for shard to start
+    let mut shard_ready = 0;
+    while running.load(Ordering::SeqCst) {
+        match status_receiver.try_recv() {
+            Ok(ShardStatus::Ready) => {
+                shard_ready += 1;
+                if shard_ready == num_shard {
+                    break;
+                }
+            }
+            Err(TryRecvError::Empty) => {
+                sleep(Duration::from_millis(100));
+            }
+            Ok(ShardStatus::Failed((rank, err))) => {
+                tracing::error!("Shard {} failed to start:\n{}", rank, err);
+                shutdown_shards(shutdown, shutdown_receiver);
+                return Err(LauncherError::ShardCannotStart);
+            }
+            Err(TryRecvError::Disconnected) => {
+                tracing::error!("Shard status channel disconnected");
+                shutdown_shards(shutdown, shutdown_receiver);
+                return Err(LauncherError::ShardDisconnected);
+            }
+        }
+    }
+    Ok(())
+}
+
+fn spawn_webserver(
+    args: Args,
+    shutdown: Arc<Mutex<bool>>,
+    shutdown_receiver: &mpsc::Receiver<()>,
+) -> Result<Popen, LauncherError> {
+    // All shard started
+    // Start webserver
+    tracing::info!("Starting Webserver");
+    let mut argv = vec![
+        "text-generation-router".to_string(),
+        "--max-concurrent-requests".to_string(),
+        args.max_concurrent_requests.to_string(),
+        "--max-best-of".to_string(),
+        args.max_best_of.to_string(),
+        "--max-stop-sequences".to_string(),
+        args.max_stop_sequences.to_string(),
+        "--max-input-length".to_string(),
+        args.max_input_length.to_string(),
+        "--max-total-tokens".to_string(),
+        args.max_total_tokens.to_string(),
+        "--waiting-served-ratio".to_string(),
+        args.waiting_served_ratio.to_string(),
+        "--max-waiting-tokens".to_string(),
+        args.max_waiting_tokens.to_string(),
+        "--port".to_string(),
+        args.port.to_string(),
+        "--master-shard-uds-path".to_string(),
+        format!("{}-0", args.shard_uds_path),
+        "--tokenizer-name".to_string(),
+        args.model_id,
+    ];
+
+    // Deprecate max_batch_size
+    if let Some(max_batch_size) = args.max_batch_size {
+        argv.push("--max-batch-size".to_string());
+        argv.push(max_batch_size.to_string())
+    } else {
+        argv.push("--max-batch-total-tokens".to_string());
+        argv.push(args.max_batch_total_tokens.to_string())
+    }
+
+    // Model optional revision
+    if let Some(ref revision) = args.revision {
+        argv.push("--revision".to_string());
+        argv.push(revision.to_string())
+    }
+
+    if args.json_output {
+        argv.push("--json-output".to_string());
+    }
+
+    // OpenTelemetry
+    if let Some(otlp_endpoint) = args.otlp_endpoint {
+        argv.push("--otlp-endpoint".to_string());
+        argv.push(otlp_endpoint);
+    }
+
+    // CORS origins
+    for origin in args.cors_allow_origin.into_iter() {
+        argv.push("--cors-allow-origin".to_string());
+        argv.push(origin);
+    }
+
+    // Copy current process env
+    let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
+
+    // Parse Inference API token
+    if let Ok(api_token) = env::var("HF_API_TOKEN") {
+        env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
+    };
+
+    let mut webserver = match Popen::create(
+        &argv,
+        PopenConfig {
+            stdout: Redirection::Pipe,
+            stderr: Redirection::Pipe,
+            // Needed for the shutdown procedure
+            setpgid: true,
+            env: Some(env),
+            ..Default::default()
+        },
+    ) {
+        Ok(p) => p,
+        Err(err) => {
+            tracing::error!("Failed to start webserver: {}", err);
+            if let PopenError::IoError(err) = err {
+                if err.kind() == io::ErrorKind::NotFound {
+                    tracing::error!("text-generation-router not found in PATH");
+                    tracing::error!("Please install it with `make install-router`")
+                }
+            } else {
+                tracing::error!("{}", err);
+            }
+
+            shutdown_shards(shutdown, shutdown_receiver);
+            return Err(LauncherError::WebserverCannotStart);
+        }
+    };
+
+    // Redirect STDOUT and STDERR to the console
+    let webserver_stdout = webserver.stdout.take().unwrap();
+    let webserver_stderr = webserver.stderr.take().unwrap();
+
+    thread::spawn(move || {
+        let stdout = BufReader::new(webserver_stdout);
+        let stderr = BufReader::new(webserver_stderr);
+        for line in stdout.lines() {
+            println!("{}", line.unwrap());
+        }
+        for line in stderr.lines() {
+            println!("{}", line.unwrap());
+        }
+    });
+    Ok(webserver)
+}
+
+fn main() -> Result<(), LauncherError> {
+    // Pattern match configuration
+    let args = Args::parse();
+
+    if args.json_output {
+        tracing_subscriber::fmt().json().init();
+    } else {
+        tracing_subscriber::fmt().compact().init();
+    }
+
+    if args.env {
+        let env_runtime = env_runtime::Env::new();
+        tracing::info!("{}", env_runtime);
+    }
+
+    tracing::info!("{:?}", args);
+
+    let num_shard = find_num_shards(args.sharded, args.num_shard);
+    if num_shard > 1 {
+        tracing::info!("Sharding model on {num_shard} processes");
+    }
+
+    // Signal handler
+    let running = Arc::new(AtomicBool::new(true));
+    let r = running.clone();
+    ctrlc::set_handler(move || {
+        r.store(false, Ordering::SeqCst);
+    })
+    .expect("Error setting Ctrl-C handler");
+
+    // Check if model_id is a local model
+    let local_path = Path::new(&args.model_id);
+    let is_local_model = local_path.exists() && local_path.is_dir();
+
+    // Download weights for sharded models
+    if !is_local_model && args.weights_cache_override.is_none() && num_shard > 1 {
+        download_model(&args, running.clone())?;
+    }
+
+    // Shared shutdown bool
+    let shutdown = Arc::new(Mutex::new(false));
+    // Shared shutdown channel
+    // When shutting down, the main thread will wait for all senders to be dropped
+    let (shutdown_sender, shutdown_receiver) = mpsc::channel();
+
+    // Shared channel to track shard status
+    let (status_sender, status_receiver) = mpsc::channel();
+
+    spawn_shards(
+        num_shard,
+        &args,
+        shutdown.clone(),
+        &shutdown_receiver,
+        shutdown_sender,
+        &status_receiver,
+        status_sender,
+        running.clone(),
+    )?;
+
+    // We might have received a termination signal
+    if !running.load(Ordering::SeqCst) {
+        shutdown_shards(shutdown, &shutdown_receiver);
+        return Ok(());
+    }
+
+    let mut webserver = spawn_webserver(args, shutdown.clone(), &shutdown_receiver)?;
+
+    // Default exit code
+    let mut exit_code = Ok(());
+
+    while running.load(Ordering::SeqCst) {
+        if let Ok(ShardStatus::Failed((rank, err))) = status_receiver.try_recv() {
+            tracing::error!("Shard {rank} failed:\n{err}");
+            exit_code = Err(LauncherError::ShardFailed);
+            break;
+        };
+
+        match webserver.poll() {
+            Some(_) => {
+                tracing::error!("Webserver Crashed");
+                shutdown_shards(shutdown, &shutdown_receiver);
+                return Err(LauncherError::WebserverFailed);
+            }
+            None => {
+                sleep(Duration::from_millis(100));
+            }
+        };
+    }
+
+    // Graceful termination
+    webserver.terminate().unwrap();
+    tracing::info!("Waiting for webserver to gracefully shutdown");
+    webserver.wait_timeout(Duration::from_secs(90)).unwrap();
+    tracing::info!("Webserver terminated");
+    shutdown_shards(shutdown, &shutdown_receiver);
+
+    exit_code
+}
diff --git a/launcher/tests/bloom_560m.json b/launcher/tests/bloom_560m.json
new file mode 100644
index 0000000000000000000000000000000000000000..96f89f6b2b9c0993a2d8943f44b98967e1507ea5
--- /dev/null
+++ b/launcher/tests/bloom_560m.json
@@ -0,0 +1,142 @@
+{
+  "generated_text": ".get(\"action\");\n        if (action == null) {\n            throw new RuntimeException",
+  "details": {
+    "finish_reason": "length",
+    "generated_tokens": 20,
+    "seed": null,
+    "prefill": [
+      {
+        "id": 10264,
+        "text": "Test",
+        "logprob": null
+      },
+      {
+        "id": 8821,
+        "text": " request",
+        "logprob": -11.894989
+      }
+    ],
+    "tokens": [
+      {
+        "id": 17,
+        "text": ".",
+        "logprob": -1.8267672,
+        "special": false
+      },
+      {
+        "id": 1587,
+        "text": "get",
+        "logprob": -2.4674969,
+        "special": false
+      },
+      {
+        "id": 11,
+        "text": "(",
+        "logprob": -1.906001,
+        "special": false
+      },
+      {
+        "id": 5,
+        "text": "\"",
+        "logprob": -1.2279545,
+        "special": false
+      },
+      {
+        "id": 4899,
+        "text": "action",
+        "logprob": -4.170299,
+        "special": false
+      },
+      {
+        "id": 5,
+        "text": "\"",
+        "logprob": -0.32478866,
+        "special": false
+      },
+      {
+        "id": 12,
+        "text": ")",
+        "logprob": -1.0773665,
+        "special": false
+      },
+      {
+        "id": 30,
+        "text": ";",
+        "logprob": -0.27640742,
+        "special": false
+      },
+      {
+        "id": 837,
+        "text": "\n       ",
+        "logprob": -1.6970354,
+        "special": false
+      },
+      {
+        "id": 1320,
+        "text": " if",
+        "logprob": -1.4495516,
+        "special": false
+      },
+      {
+        "id": 375,
+        "text": " (",
+        "logprob": -0.23609057,
+        "special": false
+      },
+      {
+        "id": 4899,
+        "text": "action",
+        "logprob": -1.1916996,
+        "special": false
+      },
+      {
+        "id": 3535,
+        "text": " ==",
+        "logprob": -0.8918753,
+        "special": false
+      },
+      {
+        "id": 5109,
+        "text": " null",
+        "logprob": -0.3933342,
+        "special": false
+      },
+      {
+        "id": 12,
+        "text": ")",
+        "logprob": -0.43212673,
+        "special": false
+      },
+      {
+        "id": 731,
+        "text": " {",
+        "logprob": -0.17702064,
+        "special": false
+      },
+      {
+        "id": 1260,
+        "text": "\n           ",
+        "logprob": -0.07027565,
+        "special": false
+      },
+      {
+        "id": 10519,
+        "text": " throw",
+        "logprob": -1.3915029,
+        "special": false
+      },
+      {
+        "id": 2084,
+        "text": " new",
+        "logprob": -0.04201372,
+        "special": false
+      },
+      {
+        "id": 150858,
+        "text": " RuntimeException",
+        "logprob": -1.7329919,
+        "special": false
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/launcher/tests/integration_tests.rs b/launcher/tests/integration_tests.rs
new file mode 100644
index 0000000000000000000000000000000000000000..0d2b6c74812fd3a2c8dfb679046dc1e765a7cf31
--- /dev/null
+++ b/launcher/tests/integration_tests.rs
@@ -0,0 +1,172 @@
+use float_eq::assert_float_eq;
+use serde::Deserialize;
+use serde_json::Value;
+use std::fs::File;
+use std::io::{BufRead, BufReader};
+use std::path::PathBuf;
+use std::thread;
+use std::thread::sleep;
+use std::time::Duration;
+use subprocess::{Popen, PopenConfig, Redirection};
+
+#[derive(Deserialize)]
+pub struct Token {
+    id: u32,
+    text: String,
+    logprob: Option<f32>,
+    special: bool,
+}
+
+#[derive(Deserialize)]
+struct Details {
+    finish_reason: String,
+    generated_tokens: u32,
+    tokens: Vec<Token>,
+}
+
+#[derive(Deserialize)]
+struct GeneratedText {
+    generated_text: String,
+    details: Details,
+}
+
+fn start_launcher(model_id: String, num_shard: usize, port: usize, master_port: usize) -> Popen {
+    let argv = vec![
+        "text-generation-launcher".to_string(),
+        "--model-id".to_string(),
+        model_id.clone(),
+        "--num-shard".to_string(),
+        num_shard.to_string(),
+        "--port".to_string(),
+        port.to_string(),
+        "--master-port".to_string(),
+        master_port.to_string(),
+        "--shard-uds-path".to_string(),
+        format!("/tmp/test-{}-{}-{}", num_shard, port, master_port),
+    ];
+
+    let mut launcher = Popen::create(
+        &argv,
+        PopenConfig {
+            stdout: Redirection::Pipe,
+            stderr: Redirection::Merge,
+            ..Default::default()
+        },
+    )
+    .expect("Could not start launcher");
+
+    // Redirect STDOUT and STDERR to the console
+    // (STDERR is merged into STDOUT)
+    let launcher_stdout = launcher.stdout.take().unwrap();
+
+    thread::spawn(move || {
+        let stdout = BufReader::new(launcher_stdout);
+        for line in stdout.lines() {
+            println!("{}", line.unwrap());
+        }
+    });
+
+    for _ in 0..60 {
+        let health = reqwest::blocking::get(format!("http://localhost:{}/health", port));
+        if health.is_ok() {
+            return launcher;
+        }
+        sleep(Duration::from_secs(2));
+    }
+
+    launcher.terminate().unwrap();
+    launcher.wait().unwrap();
+    panic!("failed to launch {}", model_id)
+}
+
+fn test_model(
+    model_id: String,
+    num_shard: usize,
+    port: usize,
+    master_port: usize,
+) -> GeneratedText {
+    let mut launcher = start_launcher(model_id, num_shard, port, master_port);
+
+    let data = r#"
+        {
+            "inputs": "Test request",
+            "parameters": {
+                "details": true
+            }
+        }"#;
+    let req: Value = serde_json::from_str(data).unwrap();
+
+    let client = reqwest::blocking::Client::new();
+    let res = client
+        .post(format!("http://localhost:{}/generate", port))
+        .json(&req)
+        .send();
+
+    launcher.terminate().unwrap();
+    launcher.wait().unwrap();
+
+    let result: GeneratedText = res.unwrap().json().unwrap();
+    result
+}
+
+fn read_json(name: &str) -> GeneratedText {
+    let mut d = PathBuf::from(env!("CARGO_MANIFEST_DIR"));
+    d.push("tests/");
+    d.push(name);
+
+    let file = File::open(d).unwrap();
+    let reader = BufReader::new(file);
+
+    let result: GeneratedText = serde_json::from_reader(reader).unwrap();
+    result
+}
+
+fn compare_results(result: GeneratedText, expected: GeneratedText) {
+    assert_eq!(result.generated_text, expected.generated_text);
+    assert_eq!(result.details.finish_reason, expected.details.finish_reason);
+    assert_eq!(
+        result.details.generated_tokens,
+        expected.details.generated_tokens
+    );
+
+    for (token, expected_token) in result
+        .details
+        .tokens
+        .into_iter()
+        .zip(expected.details.tokens.into_iter())
+    {
+        assert_eq!(token.id, expected_token.id);
+        assert_eq!(token.text, expected_token.text);
+        assert_eq!(token.special, expected_token.special);
+        if let Some(logprob) = token.logprob {
+            let expected_logprob = expected_token.logprob.unwrap();
+            assert_float_eq!(logprob, expected_logprob, abs <= 0.001);
+        } else {
+            assert_eq!(token.logprob, expected_token.logprob);
+        }
+    }
+}
+
+#[test]
+fn test_bloom_560m() {
+    let expected = read_json("bloom_560m.json");
+
+    let result = test_model("bigscience/bloom-560m".to_string(), 1, 3000, 29500);
+    compare_results(result, expected);
+}
+
+#[test]
+fn test_bloom_560m_distributed() {
+    let expected = read_json("bloom_560m.json");
+
+    let result = test_model("bigscience/bloom-560m".to_string(), 2, 3001, 29501);
+    compare_results(result, expected);
+}
+
+#[test]
+fn test_mt0_base() {
+    let expected = read_json("mt0_base.json");
+
+    let result = test_model("bigscience/mt0-base".to_string(), 1, 3002, 29502);
+    compare_results(result, expected);
+}
diff --git a/launcher/tests/mt0_base.json b/launcher/tests/mt0_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..f5be63f95593e6c7ba7f43dd1251b8d971b3ea4f
--- /dev/null
+++ b/launcher/tests/mt0_base.json
@@ -0,0 +1,137 @@
+{
+  "generated_text": "\"\"\"Test the contents of the contents of the contents. \"\"\" test_test",
+  "details": {
+    "finish_reason": "length",
+    "generated_tokens": 20,
+    "seed": null,
+    "prefill": [
+      {
+        "id": 0,
+        "text": "<pad>",
+        "logprob": null
+      }
+    ],
+    "tokens": [
+      {
+        "id": 259,
+        "text": "",
+        "logprob": -1.3656927,
+        "special": false
+      },
+      {
+        "id": 215100,
+        "text": "\"\"\"",
+        "logprob": -2.6551573,
+        "special": false
+      },
+      {
+        "id": 46138,
+        "text": "Test",
+        "logprob": -1.8059857,
+        "special": false
+      },
+      {
+        "id": 287,
+        "text": " the",
+        "logprob": -1.2102449,
+        "special": false
+      },
+      {
+        "id": 259,
+        "text": " ",
+        "logprob": -1.6057279,
+        "special": false
+      },
+      {
+        "id": 49076,
+        "text": "contents",
+        "logprob": -3.6060903,
+        "special": false
+      },
+      {
+        "id": 304,
+        "text": " of",
+        "logprob": -0.5270343,
+        "special": false
+      },
+      {
+        "id": 287,
+        "text": " the",
+        "logprob": -0.62522805,
+        "special": false
+      },
+      {
+        "id": 259,
+        "text": " ",
+        "logprob": -1.4069618,
+        "special": false
+      },
+      {
+        "id": 49076,
+        "text": "contents",
+        "logprob": -2.621994,
+        "special": false
+      },
+      {
+        "id": 304,
+        "text": " of",
+        "logprob": -1.3172221,
+        "special": false
+      },
+      {
+        "id": 287,
+        "text": " the",
+        "logprob": -0.3501925,
+        "special": false
+      },
+      {
+        "id": 259,
+        "text": " ",
+        "logprob": -0.7219573,
+        "special": false
+      },
+      {
+        "id": 49076,
+        "text": "contents",
+        "logprob": -1.0494149,
+        "special": false
+      },
+      {
+        "id": 260,
+        "text": ".",
+        "logprob": -1.0803378,
+        "special": false
+      },
+      {
+        "id": 259,
+        "text": " ",
+        "logprob": -0.32933083,
+        "special": false
+      },
+      {
+        "id": 215100,
+        "text": "\"\"\"",
+        "logprob": -0.11268901,
+        "special": false
+      },
+      {
+        "id": 2978,
+        "text": " test",
+        "logprob": -1.5846587,
+        "special": false
+      },
+      {
+        "id": 290,
+        "text": "_",
+        "logprob": -0.49796978,
+        "special": false
+      },
+      {
+        "id": 4125,
+        "text": "test",
+        "logprob": -2.0026445,
+        "special": false
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/proto/generate.proto b/proto/generate.proto
new file mode 100644
index 0000000000000000000000000000000000000000..894d7bc1827737e4b5caa7dd556318601e94bad0
--- /dev/null
+++ b/proto/generate.proto
@@ -0,0 +1,181 @@
+syntax = "proto3";
+
+package generate.v1;
+
+service TextGenerationService {
+    /// Model Info
+    rpc Info (InfoRequest) returns (InfoResponse) {}
+    /// Service discovery
+    rpc ServiceDiscovery (ServiceDiscoveryRequest) returns (ServiceDiscoveryResponse) {}
+    /// Empties batch cache
+    rpc ClearCache (ClearCacheRequest) returns (ClearCacheResponse);
+    /// Remove requests from a cached batch
+    rpc FilterBatch (FilterBatchRequest) returns (FilterBatchResponse);
+    /// Prefill batch and decode first token
+    rpc Prefill (PrefillRequest) returns (PrefillResponse);
+    /// Decode token for a list of prefilled batches
+    rpc Decode (DecodeRequest) returns (DecodeResponse);
+    /// Health check
+    rpc Health (HealthRequest) returns (HealthResponse);
+}
+
+message HealthRequest {}
+message HealthResponse {}
+
+/// Empty request
+message InfoRequest {}
+
+message InfoResponse {
+    bool requires_padding = 1;
+    string dtype = 2;
+    string device_type = 3;
+}
+
+/// Empty request
+message ServiceDiscoveryRequest {}
+
+message ServiceDiscoveryResponse {
+    /// Other shards urls
+    repeated string urls = 1;
+}
+
+message ClearCacheRequest {
+    /// Optional batch id
+    optional uint64 id = 1;
+}
+
+/// Empty response
+message ClearCacheResponse {}
+
+message NextTokenChooserParameters {
+    /// exponential scaling output probability distribution
+    float temperature = 1;
+    /// restricting to the k highest probability elements
+    uint32 top_k = 2;
+    /// restricting to top tokens summing to prob_cut_off <= prob_cut_off
+    float top_p = 3;
+    /// restricting to top tokens summing to prob_cut_off <= prob_cut_off
+    float typical_p = 4;
+    /// apply sampling on the logits
+    bool do_sample = 5;
+    /// random seed for sampling
+    uint64 seed = 6;
+    /// repetition penalty
+    float repetition_penalty = 7;
+    /// token watermarking using "A Watermark for Large Language Models"
+    bool watermark = 8;
+}
+
+message StoppingCriteriaParameters {
+    /// Maximum number of generated tokens
+    uint32 max_new_tokens = 1;
+    /// Optional stopping sequences
+    repeated string stop_sequences = 2;
+    /// Ignore end of sequence token
+    /// used for benchmarking
+    bool ignore_eos_token = 3;
+}
+
+message Request {
+    /// Request ID
+    uint64 id = 1;
+    /// The generation context
+    string inputs = 2;
+    /// Context truncation
+    uint32 truncate = 3;
+    /// Next Token Chooser Parameters
+    NextTokenChooserParameters parameters = 4;
+    /// Stopping Criteria Parameters
+    StoppingCriteriaParameters stopping_parameters = 5;
+}
+
+message Batch {
+    /// Batch ID
+    uint64 id = 1;
+    /// Individual requests
+    repeated Request requests = 2;
+    /// Batch size (==len(requests))
+    uint32 size = 3;
+    /// Maximum number of tokens this batch will grow to
+    uint32 max_tokens = 4;
+}
+
+enum FinishReason {
+    FINISH_REASON_LENGTH = 0;
+    FINISH_REASON_EOS_TOKEN = 1;
+    FINISH_REASON_STOP_SEQUENCE = 2;
+}
+
+message GeneratedText {
+    /// Output
+    string text = 1;
+    /// Number of generated tokens
+    uint32 generated_tokens = 2;
+    /// Finish reason
+    FinishReason finish_reason = 3;
+    /// Seed
+    optional uint64 seed = 4;
+}
+
+message PrefillTokens {
+    /// Prefill Token IDs
+    repeated uint32 ids = 1;
+    /// Prefill Logprobs
+    repeated float logprobs = 2;
+    /// Prefill tokens
+    repeated string texts = 3;
+}
+
+message Generation {
+    /// Request ID
+    uint64 request_id = 1;
+    /// Prefill tokens (optional)
+    PrefillTokens prefill_tokens = 2;
+    /// Token ID
+    uint32 token_id = 3;
+    /// Logprob
+    float token_logprob = 4;
+    /// Text
+    string token_text = 5;
+    /// Is it a special token
+    bool token_is_special = 6;
+    /// Complete generated text
+    GeneratedText generated_text = 7;
+}
+
+message FilterBatchRequest {
+    /// Batch ID
+    uint64 batch_id = 1;
+    /// Requests to keep
+    repeated Request keep_requests = 2;
+}
+
+message FilterBatchResponse {
+    /// Filtered Batch (cached)
+    Batch batch = 1;
+}
+
+
+message PrefillRequest {
+    /// Batch
+    Batch batch = 1;
+}
+
+message PrefillResponse {
+    /// Generation
+    repeated Generation generations = 1;
+    /// Next batch (cached)
+    optional Batch batch = 2;
+}
+
+message DecodeRequest {
+    /// Cached batches
+    repeated Batch batches = 1;
+}
+
+message DecodeResponse {
+    /// Decodes
+    repeated Generation generations = 1;
+    /// Next batch (cached)
+    optional Batch batch = 2;
+}
diff --git a/router/Cargo.toml b/router/Cargo.toml
new file mode 100644
index 0000000000000000000000000000000000000000..4fa523a54bf951759408717b0c7b095cac9fcd31
--- /dev/null
+++ b/router/Cargo.toml
@@ -0,0 +1,44 @@
+[package]
+name = "text-generation-router"
+version = "0.6.0"
+edition = "2021"
+authors = ["Olivier Dehaene"]
+description = "Text Generation Webserver"
+build = "build.rs"
+
+[lib]
+path = "src/lib.rs"
+
+[[bin]]
+name = "text-generation-router"
+path = "src/main.rs"
+
+[dependencies]
+async-stream = "0.3.3"
+axum = { version = "0.6.4", features = ["json"] }
+axum-tracing-opentelemetry = "0.10.0"
+text-generation-client = { path = "client" }
+clap = { version = "4.1.4", features = ["derive", "env"] }
+flume = "0.10.14"
+futures = "0.3.26"
+metrics = "0.20.1"
+metrics-exporter-prometheus = { version = "0.11.0", features = [] }
+nohash-hasher = "0.2.0"
+opentelemetry = { version = "0.18.0", features = ["rt-tokio"] }
+opentelemetry-otlp = "0.11.0"
+rand = "0.8.5"
+reqwest = { version = "0.11.14", features = [] }
+serde = "1.0.152"
+serde_json = "1.0.93"
+thiserror = "1.0.38"
+tokenizers = "0.13.3"
+tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
+tower-http = { version = "0.4.0", features = ["cors"] }
+tracing = "0.1.37"
+tracing-opentelemetry = "0.18.0"
+tracing-subscriber = { version = "0.3.16", features = ["json", "env-filter"] }
+utoipa = { version = "3.0.1", features = ["axum_extras"] }
+utoipa-swagger-ui = { version = "3.0.2", features = ["axum"] }
+
+[build-dependencies]
+vergen = { version = "8.0.0", features = ["build", "git", "gitcl"] }
diff --git a/router/build.rs b/router/build.rs
new file mode 100644
index 0000000000000000000000000000000000000000..f5eb8a2657ac594e889ae971480b3c3ab34186a1
--- /dev/null
+++ b/router/build.rs
@@ -0,0 +1,26 @@
+use std::error::Error;
+use vergen::EmitBuilder;
+
+fn main() -> Result<(), Box<dyn Error>> {
+    // Try to get the git sha from the local git repository
+    if EmitBuilder::builder()
+        .fail_on_error()
+        .git_sha(false)
+        .emit()
+        .is_err()
+    {
+        // Unable to get the git sha
+        if let Ok(sha) = std::env::var("GIT_SHA") {
+            // Set it from an env var
+            println!("cargo:rustc-env=VERGEN_GIT_SHA={sha}");
+        }
+    }
+
+    // Set docker label if present
+    if let Ok(label) = std::env::var("DOCKER_LABEL") {
+        // Set it from an env var
+        println!("cargo:rustc-env=DOCKER_LABEL={label}");
+    }
+
+    Ok(())
+}
diff --git a/router/client/Cargo.toml b/router/client/Cargo.toml
new file mode 100644
index 0000000000000000000000000000000000000000..b32402a559f7cd699a132057a5abc74fe3792656
--- /dev/null
+++ b/router/client/Cargo.toml
@@ -0,0 +1,19 @@
+[package]
+name = "text-generation-client"
+version = "0.6.0"
+edition = "2021"
+
+[dependencies]
+futures = "^0.3"
+grpc-metadata = { path = "../grpc-metadata" }
+prost = "^0.11"
+thiserror = "^1.0"
+tokio = { version = "^1.25", features = ["sync"] }
+tonic = "^0.8"
+tower = "^0.4"
+tracing = "^0.1"
+tracing-error = "^0.2"
+
+[build-dependencies]
+tonic-build = "0.8.4"
+prost-build = "0.11.6"
diff --git a/router/client/build.rs b/router/client/build.rs
new file mode 100644
index 0000000000000000000000000000000000000000..497be545fbc950a90a58a34b2e16a1845e800913
--- /dev/null
+++ b/router/client/build.rs
@@ -0,0 +1,19 @@
+use std::fs;
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    println!("cargo:rerun-if-changed=../../proto/generate.proto");
+    fs::create_dir("src/pb").unwrap_or(());
+
+    let mut config = prost_build::Config::new();
+    config.protoc_arg("--experimental_allow_proto3_optional");
+
+    tonic_build::configure()
+        .build_client(true)
+        .build_server(false)
+        .out_dir("src/pb")
+        .include_file("mod.rs")
+        .compile_with_config(config, &["../../proto/generate.proto"], &["../../proto"])
+        .unwrap_or_else(|e| panic!("protobuf compilation failed: {e}"));
+
+    Ok(())
+}
diff --git a/router/client/src/client.rs b/router/client/src/client.rs
new file mode 100644
index 0000000000000000000000000000000000000000..bf1b6b58112893e48b276b1af90e47938007722f
--- /dev/null
+++ b/router/client/src/client.rs
@@ -0,0 +1,121 @@
+/// Single shard Client
+use crate::pb::generate::v1::text_generation_service_client::TextGenerationServiceClient;
+use crate::pb::generate::v1::*;
+use crate::Result;
+use grpc_metadata::InjectTelemetryContext;
+use tonic::transport::{Channel, Uri};
+use tracing::instrument;
+
+/// Text Generation Inference gRPC client
+#[derive(Debug, Clone)]
+pub struct Client {
+    stub: TextGenerationServiceClient<Channel>,
+}
+
+impl Client {
+    /// Returns a client connected to the given url
+    pub async fn connect(uri: Uri) -> Result<Self> {
+        let channel = Channel::builder(uri).connect().await?;
+
+        Ok(Self {
+            stub: TextGenerationServiceClient::new(channel),
+        })
+    }
+
+    /// Returns a client connected to the given unix socket
+    pub async fn connect_uds(path: String) -> Result<Self> {
+        let channel = Channel::from_shared("http://[::]:50051".to_string())
+            .unwrap()
+            .connect_with_connector(tower::service_fn(move |_: Uri| {
+                tokio::net::UnixStream::connect(path.clone())
+            }))
+            .await?;
+
+        Ok(Self {
+            stub: TextGenerationServiceClient::new(channel),
+        })
+    }
+
+    /// Returns a list of uris or unix sockets of all shards
+    #[instrument(skip(self))]
+    pub async fn service_discovery(&mut self) -> Result<Vec<String>> {
+        let request = tonic::Request::new(ServiceDiscoveryRequest {}).inject_context();
+        let response = self.stub.service_discovery(request).await?;
+        let urls = response
+            .into_inner()
+            .urls
+            .into_iter()
+            // Remove unix socket prefix
+            .map(|url| match url.strip_prefix("unix://") {
+                None => url,
+                Some(stripped_url) => stripped_url.to_string(),
+            })
+            .collect();
+        Ok(urls)
+    }
+
+    /// Get model info
+    #[instrument(skip(self))]
+    pub async fn info(&mut self) -> Result<InfoResponse> {
+        let request = tonic::Request::new(InfoRequest {}).inject_context();
+        let response = self.stub.info(request).await?.into_inner();
+        Ok(response)
+    }
+
+    /// Get model health
+    #[instrument(skip(self))]
+    pub async fn health(&mut self) -> Result<HealthResponse> {
+        let request = tonic::Request::new(HealthRequest {}).inject_context();
+        let response = self.stub.health(request).await?.into_inner();
+        Ok(response)
+    }
+
+    /// Clear the past generations cache
+    #[instrument(skip(self))]
+    pub async fn clear_cache(&mut self, batch_id: Option<u64>) -> Result<()> {
+        let request = tonic::Request::new(ClearCacheRequest { id: batch_id }).inject_context();
+        self.stub.clear_cache(request).await?;
+        Ok(())
+    }
+
+    /// Filter a cached batch
+    #[instrument(skip(self))]
+    pub async fn filter_batch(
+        &mut self,
+        batch_id: u64,
+        keep_requests: Vec<Request>,
+    ) -> Result<Option<Batch>> {
+        let request = tonic::Request::new(FilterBatchRequest {
+            batch_id,
+            keep_requests,
+        })
+        .inject_context();
+        let filtered_batch = self.stub.filter_batch(request).await?.into_inner();
+        Ok(filtered_batch.batch)
+    }
+
+    /// Generate one token for each request in the given batch
+    ///
+    /// Returns Generation for each request in batch
+    /// and the next cached batch
+    #[instrument(skip_all, fields(id = &batch.id, size = &batch.size))]
+    pub async fn prefill(&mut self, batch: Batch) -> Result<(Vec<Generation>, Option<Batch>)> {
+        let request = tonic::Request::new(PrefillRequest { batch: Some(batch) }).inject_context();
+        let response = self.stub.prefill(request).await?.into_inner();
+        Ok((response.generations, response.batch))
+    }
+
+    /// Generate one token for each request in the given cached batches
+    ///
+    /// Returns Generation for each request in batches
+    /// and the next cached batch
+    #[instrument(skip_all, fields(size = batches.iter().map(|batch|{batch.size}).sum::<u32>()))]
+    pub async fn decode(
+        &mut self,
+        batches: Vec<Batch>,
+    ) -> Result<(Vec<Generation>, Option<Batch>)> {
+        let request = tonic::Request::new(DecodeRequest { batches }).inject_context();
+        let response = self.stub.decode(request).await?.into_inner();
+        Ok((response.generations, response.batch))
+    }
+}
diff --git a/router/client/src/lib.rs b/router/client/src/lib.rs
new file mode 100644
index 0000000000000000000000000000000000000000..401082c57692855597ca9c93f2bd20aca8674877
--- /dev/null
+++ b/router/client/src/lib.rs
@@ -0,0 +1,44 @@
+//! Text Generation gRPC client library
+
+mod client;
+#[allow(clippy::derive_partial_eq_without_eq)]
+mod pb;
+mod sharded_client;
+
+pub use client::Client;
+pub use pb::generate::v1::HealthResponse;
+pub use pb::generate::v1::InfoResponse as ShardInfo;
+pub use pb::generate::v1::{
+    Batch, FinishReason, GeneratedText, Generation, NextTokenChooserParameters, PrefillTokens,
+    Request, StoppingCriteriaParameters,
+};
+pub use sharded_client::ShardedClient;
+use thiserror::Error;
+use tonic::transport;
+use tonic::Status;
+
+#[derive(Error, Debug, Clone)]
+pub enum ClientError {
+    #[error("Could not connect to Text Generation server: {0}")]
+    Connection(String),
+    #[error("Server error: {0}")]
+    Generation(String),
+}
+
+impl From<Status> for ClientError {
+    fn from(err: Status) -> Self {
+        let err = Self::Generation(err.message().to_string());
+        tracing::error!("{err}");
+        err
+    }
+}
+
+impl From<transport::Error> for ClientError {
+    fn from(err: transport::Error) -> Self {
+        let err = Self::Connection(err.to_string());
+        tracing::error!("{err}");
+        err
+    }
+}
+
+pub type Result<T> = std::result::Result<T, ClientError>;
diff --git a/router/client/src/sharded_client.rs b/router/client/src/sharded_client.rs
new file mode 100644
index 0000000000000000000000000000000000000000..73524740dc82dbad2e6a41df4f5a1bbe23eb126e
--- /dev/null
+++ b/router/client/src/sharded_client.rs
@@ -0,0 +1,122 @@
+/// Multi shard Client
+use crate::Result;
+use crate::{Batch, Client, Generation, HealthResponse, Request, ShardInfo};
+use futures::future::join_all;
+use tonic::transport::Uri;
+use tracing::instrument;
+
+#[derive(Debug, Clone)]
+/// Text Generation Inference gRPC multi client
+pub struct ShardedClient {
+    clients: Vec<Client>,
+}
+
+impl ShardedClient {
+    fn new(clients: Vec<Client>) -> Self {
+        Self { clients }
+    }
+
+    /// Create a new ShardedClient from a master client. The master client will communicate with
+    /// the other shards and returns all uris/unix sockets with the `service_discovery` gRPC method.
+    async fn from_master_client(mut master_client: Client) -> Result<Self> {
+        // Get all uris/unix sockets from the master client
+        let uris = master_client.service_discovery().await?;
+        let futures = uris.into_iter().map(Client::connect_uds);
+        let clients: Result<Vec<Client>> = join_all(futures).await.into_iter().collect();
+        Ok(Self::new(clients?))
+    }
+
+    /// Returns a client connected to the given uri
+    pub async fn connect(uri: Uri) -> Result<Self> {
+        let master_client = Client::connect(uri).await?;
+        Self::from_master_client(master_client).await
+    }
+
+    /// Returns a client connected to the given unix socket
+    pub async fn connect_uds(path: String) -> Result<Self> {
+        let master_client = Client::connect_uds(path).await?;
+        Self::from_master_client(master_client).await
+    }
+
+    /// Get the model info
+    #[instrument(skip(self))]
+    pub async fn info(&mut self) -> Result<ShardInfo> {
+        let futures: Vec<_> = self
+            .clients
+            .iter_mut()
+            .map(|client| client.info())
+            .collect();
+        join_all(futures).await.pop().unwrap()
+    }
+
+    /// GRPC health check
+    #[instrument(skip(self))]
+    pub async fn health(&mut self) -> Result<HealthResponse> {
+        let futures: Vec<_> = self
+            .clients
+            .iter_mut()
+            .map(|client| client.health())
+            .collect();
+        join_all(futures).await.pop().unwrap()
+    }
+
+    /// Clear the past generations cache
+    #[instrument(skip(self))]
+    pub async fn clear_cache(&mut self, batch_id: Option<u64>) -> Result<()> {
+        let futures: Vec<_> = self
+            .clients
+            .iter_mut()
+            .map(|client| client.clear_cache(batch_id))
+            .collect();
+        join_all(futures).await.into_iter().collect()
+    }
+
+    /// Filter a cached batch
+    #[instrument(skip(self))]
+    pub async fn filter_batch(
+        &mut self,
+        batch_id: u64,
+        keep_requests: Vec<Request>,
+    ) -> Result<Option<Batch>> {
+        let futures: Vec<_> = self
+            .clients
+            .iter_mut()
+            .map(|client| Box::pin(client.filter_batch(batch_id, keep_requests.clone())))
+            .collect();
+        // all shards return the same message
+        join_all(futures).await.pop().unwrap()
+    }
+
+    /// Generate one token for each request in the given batch
+    ///
+    /// Returns Generation for each request in batch
+    /// and the next cached batch
+    #[instrument(skip_all, fields(id = &batch.id, size = &batch.size))]
+    pub async fn prefill(&mut self, batch: Batch) -> Result<(Vec<Generation>, Option<Batch>)> {
+        let futures: Vec<_> = self
+            .clients
+            .iter_mut()
+            .map(|client| Box::pin(client.prefill(batch.clone())))
+            .collect();
+        // all shards return the same message
+        join_all(futures).await.pop().unwrap()
+    }
+
+    /// Generate one token for each request in the given cached batches
+    ///
+    /// Returns Generation for each request in batches
+    /// and the next cached batch
+    #[instrument(skip_all, fields(size = batches.iter().map(|batch|{batch.size}).sum::<u32>()))]
+    pub async fn decode(
+        &mut self,
+        batches: Vec<Batch>,
+    ) -> Result<(Vec<Generation>, Option<Batch>)> {
+        let futures: Vec<_> = self
+            .clients
+            .iter_mut()
+            .map(|client| Box::pin(client.decode(batches.clone())))
+            .collect();
+        // all shards return the same message
+        join_all(futures).await.pop().unwrap()
+    }
+}
diff --git a/router/grpc-metadata/Cargo.toml b/router/grpc-metadata/Cargo.toml
new file mode 100644
index 0000000000000000000000000000000000000000..311092e3ffc288e95569f538307d05173bc53006
--- /dev/null
+++ b/router/grpc-metadata/Cargo.toml
@@ -0,0 +1,10 @@
+[package]
+name = "grpc-metadata"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+opentelemetry = "0.18.0"
+tonic = "^0.8"
+tracing = "^0.1"
+tracing-opentelemetry = "0.18.0"
diff --git a/router/grpc-metadata/src/lib.rs b/router/grpc-metadata/src/lib.rs
new file mode 100644
index 0000000000000000000000000000000000000000..7ba353fa5e7a090f21a7491eda03b58a9512580e
--- /dev/null
+++ b/router/grpc-metadata/src/lib.rs
@@ -0,0 +1,62 @@
+//! A crate to extract and inject a OpenTelemetry context from and to a gRPC request.
+//! Inspired by: https://github.com/open-telemetry/opentelemetry-rust gRPC examples
+
+use opentelemetry::global;
+use opentelemetry::propagation::{Extractor, Injector};
+use tracing_opentelemetry::OpenTelemetrySpanExt;
+
+/// Extract context metadata from a gRPC request's metadata
+struct MetadataExtractor<'a>(pub &'a tonic::metadata::MetadataMap);
+
+impl<'a> Extractor for MetadataExtractor<'a> {
+    /// Get a value for a key from the MetadataMap.  If the value can't be converted to &str, returns None
+    fn get(&self, key: &str) -> Option<&str> {
+        self.0.get(key).and_then(|metadata| metadata.to_str().ok())
+    }
+
+    /// Collect all the keys from the MetadataMap.
+    fn keys(&self) -> Vec<&str> {
+        self.0
+            .keys()
+            .map(|key| match key {
+                tonic::metadata::KeyRef::Ascii(v) => v.as_str(),
+                tonic::metadata::KeyRef::Binary(v) => v.as_str(),
+            })
+            .collect::<Vec<_>>()
+    }
+}
+
+/// Inject context in the metadata of a gRPC request.
+struct MetadataInjector<'a>(pub &'a mut tonic::metadata::MetadataMap);
+
+impl<'a> Injector for MetadataInjector<'a> {
+    /// Set a key and value in the MetadataMap.  Does nothing if the key or value are not valid inputs
+    fn set(&mut self, key: &str, value: String) {
+        if let Ok(key) = tonic::metadata::MetadataKey::from_bytes(key.as_bytes()) {
+            if let Ok(val) = value.parse() {
+                self.0.insert(key, val);
+            }
+        }
+    }
+}
+
+/// Get a context from the global context and inject the span into a gRPC request's metadata.
+fn inject(metadata: &mut tonic::metadata::MetadataMap) {
+    global::get_text_map_propagator(|propagator| {
+        propagator.inject_context(
+            &tracing::Span::current().context(),
+            &mut MetadataInjector(metadata),
+        )
+    })
+}
+
+pub trait InjectTelemetryContext {
+    fn inject_context(self) -> Self;
+}
+
+impl<T> InjectTelemetryContext for tonic::Request<T> {
+    fn inject_context(mut self) -> Self {
+        inject(self.metadata_mut());
+        self
+    }
+}
diff --git a/router/src/health.rs b/router/src/health.rs
new file mode 100644
index 0000000000000000000000000000000000000000..45f50e9ddf361dd8a1f129f6d93153911f5da9e1
--- /dev/null
+++ b/router/src/health.rs
@@ -0,0 +1,66 @@
+use std::sync::atomic::{AtomicBool, Ordering};
+use std::sync::Arc;
+use text_generation_client::{
+    Batch, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
+};
+
+// Note: Request ids and batch ids cannot collide.
+const LIVENESS_ID: u64 = u64::MAX;
+const BATCH_ID: u64 = u64::MAX;
+
+#[derive(Clone, Debug)]
+pub(crate) struct Health {
+    client: ShardedClient,
+    generation_health: Arc<AtomicBool>,
+}
+
+impl Health {
+    pub(crate) fn new(client: ShardedClient, generation_health: Arc<AtomicBool>) -> Self {
+        Self {
+            client,
+            generation_health,
+        }
+    }
+
+    pub(crate) async fn check(&mut self) -> bool {
+        if self.generation_health.load(Ordering::SeqCst) {
+            // Generation is healthy, we only check that the shards are answering gRPC calls
+            self.client.health().await.is_ok()
+        } else {
+            // Generation is unhealthy or have not sent any generation request yet
+
+            // Dummy batch of 1 token and 1 generated token
+            let liveness_request = Request {
+                id: LIVENESS_ID,
+                inputs: "liveness".to_string(),
+                truncate: 10,
+                parameters: Some(NextTokenChooserParameters {
+                    temperature: 1.0,
+                    top_k: 0,
+                    top_p: 1.0,
+                    typical_p: 1.0,
+                    do_sample: false,
+                    seed: 0,
+                    repetition_penalty: 1.0,
+                    watermark: false,
+                }),
+                stopping_parameters: Some(StoppingCriteriaParameters {
+                    max_new_tokens: 1,
+                    stop_sequences: vec![],
+                    ignore_eos_token: false,
+                }),
+            };
+            let batch = Batch {
+                id: BATCH_ID,
+                requests: vec![liveness_request],
+                size: 1,
+                max_tokens: 2,
+            };
+            // Skips the queue
+            let value = self.client.prefill(batch).await.is_ok();
+            // Update generation health
+            self.generation_health.store(value, Ordering::SeqCst);
+            value
+        }
+    }
+}
diff --git a/router/src/infer.rs b/router/src/infer.rs
new file mode 100644
index 0000000000000000000000000000000000000000..313ec3e1c4e430e859f513e12662a0649d6e195a
--- /dev/null
+++ b/router/src/infer.rs
@@ -0,0 +1,588 @@
+/// Batching and inference logic
+use crate::validation::{Validation, ValidationError};
+use crate::{Entry, Queue, Token};
+use crate::{GenerateRequest, PrefillToken};
+use flume::r#async::RecvStream;
+use flume::SendError;
+use futures::future::try_join_all;
+use futures::stream::StreamExt;
+use nohash_hasher::IntMap;
+use std::sync::{
+    atomic::{AtomicBool, Ordering},
+    Arc,
+};
+use text_generation_client::{
+    Batch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
+};
+use thiserror::Error;
+use tokio::sync::{Notify, OwnedSemaphorePermit, Semaphore, TryAcquireError};
+use tokio::time::Instant;
+use tracing::{info_span, instrument, Instrument, Span};
+
+/// Inference struct
+#[derive(Clone)]
+pub struct Infer {
+    /// Validation
+    validation: Validation,
+    /// Request queue
+    queue: Queue,
+    /// Shared state
+    shared: Arc<Shared>,
+    /// Inference limit
+    limit_concurrent_requests: Arc<Semaphore>,
+}
+
+/// Infer shared state
+struct Shared {
+    /// Batching background Tokio task notifier
+    batching_task: Notify,
+}
+
+impl Infer {
+    #[allow(clippy::too_many_arguments)]
+    pub(crate) fn new(
+        client: ShardedClient,
+        validation: Validation,
+        waiting_served_ratio: f32,
+        max_batch_total_tokens: u32,
+        max_waiting_tokens: usize,
+        max_concurrent_requests: usize,
+        requires_padding: bool,
+        generation_health: Arc<AtomicBool>,
+    ) -> Self {
+        // Infer shared state
+        let queue = Queue::new(requires_padding);
+        let shared = Arc::new(Shared {
+            batching_task: Notify::new(),
+        });
+
+        // Spawn batching background task that contains all the inference logic
+        tokio::spawn(batching_task(
+            client,
+            waiting_served_ratio,
+            max_batch_total_tokens,
+            max_waiting_tokens,
+            queue.clone(),
+            shared.clone(),
+            generation_health,
+        ));
+
+        // Inference limit with a semaphore
+        let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
+
+        Self {
+            validation,
+            queue,
+            shared,
+            limit_concurrent_requests: semaphore,
+        }
+    }
+
+    /// Add a new request to the queue and return a stream of InferStreamResponse
+    #[instrument(skip(self))]
+    pub(crate) async fn generate_stream(
+        &self,
+        request: GenerateRequest,
+    ) -> Result<
+        (
+            OwnedSemaphorePermit,
+            RecvStream<Result<InferStreamResponse, InferError>>,
+        ),
+        InferError,
+    > {
+        // Limit concurrent requests by acquiring a permit from the semaphore
+        let permit = self
+            .clone()
+            .limit_concurrent_requests
+            .try_acquire_owned()
+            .map_err(|err| {
+                metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
+                tracing::error!("{err}");
+                err
+            })?;
+
+        // Validate request
+        let valid_request = self.validation.validate(request).await.map_err(|err| {
+            metrics::increment_counter!("tgi_request_failure", "err" => "validation");
+            tracing::error!("{err}");
+            err
+        })?;
+
+        // MPSC channel to communicate with the background batching task
+        let (response_tx, response_rx) = flume::unbounded();
+
+        // Append the request to the queue
+        self.queue.append(Entry {
+            request: valid_request,
+            response_tx,
+            span: Span::current(),
+            temp_span: None,
+            queue_time: Instant::now(),
+            batch_time: None,
+        });
+
+        // Notify the background task that we have a new entry in the queue that needs
+        // to be batched
+        self.shared.batching_task.notify_one();
+
+        // Return stream
+        Ok((permit, response_rx.into_stream()))
+    }
+
+    /// Add a new request to the queue and return a InferResponse
+    #[instrument(skip(self))]
+    pub(crate) async fn generate(
+        &self,
+        request: GenerateRequest,
+    ) -> Result<InferResponse, InferError> {
+        // Create stream and keep semaphore permit as long as generate lives
+        let (_permit, mut stream) = self.generate_stream(request).await?;
+
+        // Return values
+        let mut result_prefill = Vec::new();
+        let mut result_tokens = Vec::new();
+        let mut result_generated_text = None;
+        let mut result_start = None;
+        let mut result_queued = None;
+
+        // Iterate on stream
+        while let Some(response) = stream.next().await {
+            match response? {
+                // Add prefill tokens
+                InferStreamResponse::Prefill(tokens) => {
+                    // Create Token objects
+                    // We do that here instead of in the Python code as Rust for loops are faster
+                    result_prefill = tokens
+                        .ids
+                        .into_iter()
+                        .zip(tokens.logprobs.into_iter())
+                        .zip(tokens.texts.into_iter())
+                        .map(|((id, logprob), text)| PrefillToken { id, text, logprob })
+                        .collect();
+                }
+                // Push last token
+                InferStreamResponse::Token(token) => result_tokens.push(token),
+                // Final message
+                // Set return values
+                InferStreamResponse::End {
+                    token,
+                    generated_text,
+                    start,
+                    queued,
+                } => {
+                    result_tokens.push(token);
+                    result_generated_text = Some(generated_text);
+                    result_start = Some(start);
+                    result_queued = Some(queued)
+                }
+            }
+        }
+
+        // Check that we received a `InferStreamResponse::End` message
+        if let (Some(generated_text), Some(queued), Some(start)) =
+            (result_generated_text, result_queued, result_start)
+        {
+            Ok(InferResponse {
+                prefill: result_prefill,
+                tokens: result_tokens,
+                generated_text,
+                queued,
+                start,
+            })
+        } else {
+            let err = InferError::IncompleteGeneration;
+            metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
+            tracing::error!("{err}");
+            Err(err)
+        }
+    }
+    /// Add best_of new requests to the queue and return a InferResponse of the sequence with
+    /// the highest log probability per token
+    #[instrument(skip(self))]
+    pub(crate) async fn generate_best_of(
+        &self,
+        request: GenerateRequest,
+        best_of: usize,
+    ) -> Result<(InferResponse, Vec<InferResponse>), InferError> {
+        // validate  best_of parameter separately
+        let best_of = self.validation.validate_best_of(best_of)?;
+
+        // create multiple generate requests
+        let mut infer_responses: Vec<InferResponse> =
+            try_join_all((0..best_of).map(|_| self.generate(request.clone()))).await?;
+
+        // get the sequence with the highest log probability per token
+        let mut max_index = 0;
+        let mut max_logprob: f32 = f32::MIN;
+
+        for (i, response) in infer_responses.iter().enumerate() {
+            // mean logprobs of the generated tokens
+            let sequence_logprob = response
+                .tokens
+                .iter()
+                .map(|token| token.logprob)
+                .sum::<f32>()
+                / response.tokens.len() as f32;
+
+            // set best sequence
+            if sequence_logprob > max_logprob {
+                max_index = i;
+                max_logprob = sequence_logprob;
+            }
+        }
+        let best_response = infer_responses.remove(max_index);
+        Ok((best_response, infer_responses))
+    }
+}
+
+/// Batching logic
+/// Will be launched in a background Tokio task
+///
+/// Batches requests and sends them to the inference server
+async fn batching_task(
+    mut client: ShardedClient,
+    waiting_served_ratio: f32,
+    max_batch_total_tokens: u32,
+    max_waiting_tokens: usize,
+    queue: Queue,
+    shared: Arc<Shared>,
+    generation_health: Arc<AtomicBool>,
+) {
+    // Infinite loop
+    loop {
+        // Wait for a notification from the Infer struct
+        shared.batching_task.notified().await;
+
+        // Get the next batch from the queue
+        // This batch might be smaller than the maximum batch size if there are not enough requests
+        // waiting in the queue
+        while let Some((mut entries, batch, span)) =
+            queue.next_batch(None, max_batch_total_tokens).await
+        {
+            let mut cached_batch = prefill(&mut client, batch, &mut entries, &generation_health)
+                .instrument(span)
+                .await;
+            let mut waiting_tokens = 1;
+
+            // We loop until we do not receive any cached batch from the inference server (== until
+            // all requests have met their stopping criteria)
+            while let Some(batch) = cached_batch {
+                // Get current batch info
+                let batch_size = batch.size;
+                let batch_max_tokens = batch.max_tokens;
+                let mut batches = vec![batch];
+                metrics::gauge!("tgi_batch_current_size", batch_size as f64);
+                metrics::gauge!("tgi_batch_current_max_tokens", batch_max_tokens as f64);
+
+                let min_size = if waiting_tokens >= max_waiting_tokens {
+                    // If we didn't onboard any new requests since >= max_waiting_tokens, we try
+                    // to add a new batch even though its size might be small
+                    None
+                } else {
+                    // Minimum batch size
+                    Some((batch_size as f32 * waiting_served_ratio).floor() as usize)
+                };
+
+                let token_budget = max_batch_total_tokens - batch_max_tokens;
+
+                // Try to get a new batch
+                if let Some((mut new_entries, new_batch, span)) =
+                    queue.next_batch(min_size, token_budget).await
+                {
+                    // Tracking metrics
+                    if min_size.is_some() {
+                        metrics::increment_counter!("tgi_batch_concat", "reason" => "backpressure");
+                    } else {
+                        metrics::increment_counter!("tgi_batch_concat", "reason" => "wait_exceeded");
+                    }
+
+                    entries.iter_mut().for_each(|(_, entry)| {
+                        // Create a new span to add the info that this entry is waiting
+                        // because a new batch is being computed
+                        let entry_waiting_span = info_span!(parent: &entry.span, "waiting");
+                        // Add relationships
+                        span.follows_from(&entry_waiting_span);
+                        entry_waiting_span.follows_from(&span);
+                        // Update entry
+                        entry.temp_span = Some(entry_waiting_span);
+                    });
+
+                    // Generate one token for this new batch to have the attention past in cache
+                    let new_cached_batch =
+                        prefill(&mut client, new_batch, &mut new_entries, &generation_health)
+                            .instrument(span)
+                            .await;
+                    // Reset waiting counter
+                    waiting_tokens = 1;
+                    // Extend current batch with the new batch
+                    if let Some(new_cached_batch) = new_cached_batch {
+                        entries.extend(new_entries);
+                        batches.push(new_cached_batch);
+                    }
+                }
+
+                // Create span for this batch to add context to inference calls
+                let next_batch_size = entries.len();
+                let next_batch_span =
+                    info_span!(parent: None, "batch", batch_size = next_batch_size);
+                entries.iter_mut().for_each(|(_, entry)| {
+                    // Create a new span to link the batch back to this entry
+                    let entry_batch_span = info_span!(parent: &entry.span, "infer");
+                    // Add relationships
+                    next_batch_span.follows_from(&entry_batch_span);
+                    entry_batch_span.follows_from(&next_batch_span);
+                    // Update entry
+                    entry.temp_span = Some(entry_batch_span);
+                });
+
+                cached_batch = decode(&mut client, batches, &mut entries, &generation_health)
+                    .instrument(next_batch_span)
+                    .await;
+                waiting_tokens += 1;
+            }
+            metrics::gauge!("tgi_batch_current_size", 0.0);
+            metrics::gauge!("tgi_batch_current_max_tokens", 0.0);
+        }
+    }
+}
+
+#[instrument(skip_all)]
+async fn prefill(
+    client: &mut ShardedClient,
+    batch: Batch,
+    entries: &mut IntMap<u64, Entry>,
+    generation_health: &Arc<AtomicBool>,
+) -> Option<Batch> {
+    let start_time = Instant::now();
+    let batch_id = batch.id;
+    metrics::increment_counter!("tgi_batch_inference_count", "method" => "prefill");
+
+    match client.prefill(batch).await {
+        Ok((generations, next_batch)) => {
+            // Update health
+            generation_health.store(true, Ordering::SeqCst);
+            // Send generated tokens and filter stopped entries
+            filter_send_generations(generations, entries);
+
+            // Filter next batch and remove requests that were stopped
+            let next_batch = filter_batch(client, next_batch, entries).await;
+
+            metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "prefill");
+            metrics::increment_counter!("tgi_batch_inference_success", "method" => "prefill");
+            next_batch
+        }
+        // If we have an error, we discard the whole batch
+        Err(err) => {
+            // Update health
+            generation_health.store(false, Ordering::SeqCst);
+            let _ = client.clear_cache(Some(batch_id)).await;
+            send_errors(err, entries);
+            metrics::increment_counter!("tgi_batch_inference_failure", "method" => "prefill");
+            None
+        }
+    }
+}
+
+#[instrument(skip_all)]
+async fn decode(
+    client: &mut ShardedClient,
+    batches: Vec<Batch>,
+    entries: &mut IntMap<u64, Entry>,
+    generation_health: &Arc<AtomicBool>,
+) -> Option<Batch> {
+    let start_time = Instant::now();
+    let batch_ids: Vec<u64> = batches.iter().map(|b| b.id).collect();
+    metrics::increment_counter!("tgi_batch_inference_count", "method" => "decode");
+
+    match client.decode(batches).await {
+        Ok((generations, next_batch)) => {
+            // Update health
+            generation_health.store(true, Ordering::SeqCst);
+            // Send generated tokens and filter stopped entries
+            filter_send_generations(generations, entries);
+
+            // Filter next batch and remove requests that were stopped
+            let next_batch = filter_batch(client, next_batch, entries).await;
+
+            metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "decode");
+            metrics::increment_counter!("tgi_batch_inference_success", "method" => "decode");
+            next_batch
+        }
+        // If we have an error, we discard the whole batch
+        Err(err) => {
+            generation_health.store(false, Ordering::SeqCst);
+            for id in batch_ids {
+                let _ = client.clear_cache(Some(id)).await;
+            }
+            send_errors(err, entries);
+            metrics::increment_counter!("tgi_batch_inference_failure", "method" => "decode");
+            None
+        }
+    }
+}
+
+/// Filter a `batch` and remove all requests not present in `entries`
+#[instrument(skip_all)]
+async fn filter_batch(
+    client: &mut ShardedClient,
+    next_batch: Option<Batch>,
+    entries: &IntMap<u64, Entry>,
+) -> Option<Batch> {
+    let mut batch = next_batch?;
+
+    // No need to filter
+    if batch.size as usize == entries.len() {
+        return Some(batch);
+    }
+
+    let id = batch.id;
+
+    // Retain only requests that are still in entries
+    batch.requests.retain(|r| entries.contains_key(&r.id));
+
+    if batch.requests.is_empty() {
+        // All requests have been filtered out
+        // Next batch is now empty
+        // Clear it from the Python shards cache
+        // We unwrap here as we need to panic since we cannot recover if this method fails
+        client.clear_cache(Some(id)).await.unwrap();
+        None
+    } else {
+        // Filter Python shard cache
+        // We unwrap here as we need to panic since we cannot recover if this method fails
+        client.filter_batch(id, batch.requests).await.unwrap()
+    }
+}
+
+/// Send one or multiple `InferStreamResponse` to Infer for all `entries`
+/// and filter entries
+#[instrument(skip_all)]
+fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u64, Entry>) {
+    generations.into_iter().for_each(|generation| {
+        let id = generation.request_id;
+        // Get entry
+        // We can `expect` here as the request id should always be in the entries
+        let entry = entries
+            .get(&id)
+            .expect("ID not found in entries. This is a bug.");
+
+        // Create and enter a span to link this function back to the entry
+        let _span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_generation", generation = ?generation).entered();
+        // Send generation responses back to the infer task
+        // If the receive an error from the Flume channel, it means that the client dropped the
+        // request and we need to stop generating hence why we unwrap_or(true)
+        let stopped = send_responses(generation, entry).map_err(|err| {
+            metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
+            err
+        }).unwrap_or(true);
+        if stopped {
+            entries.remove(&id).expect("ID not found in entries. This is a bug.");
+        }
+    });
+}
+
+/// Send responses through the `entry` response channel
+fn send_responses(
+    generation: Generation,
+    entry: &Entry,
+) -> Result<bool, SendError<Result<InferStreamResponse, InferError>>> {
+    let mut stopped = false;
+
+    if let Some(prefill_tokens) = generation.prefill_tokens {
+        // Send message
+        entry
+            .response_tx
+            .send(Ok(InferStreamResponse::Prefill(prefill_tokens)))?;
+    }
+
+    // Create last Token
+    let token = Token {
+        id: generation.token_id,
+        text: generation.token_text,
+        logprob: generation.token_logprob,
+        special: generation.token_is_special,
+    };
+
+    if let Some(generated_text) = generation.generated_text {
+        // Generation has ended
+        stopped = true;
+        // Send message
+        entry.response_tx.send(Ok(InferStreamResponse::End {
+            token,
+            generated_text,
+            queued: entry.queue_time,
+            start: entry.batch_time.unwrap(),
+        }))?;
+    } else {
+        // Send message
+        entry
+            .response_tx
+            .send(Ok(InferStreamResponse::Token(token)))?;
+    }
+    Ok(stopped)
+}
+
+/// Send errors to Infer for all `entries`
+#[instrument(skip_all)]
+fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
+    entries.drain().for_each(|(_, entry)| {
+        // Create and enter a span to link this function back to the entry
+        let _send_error_span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_error").entered();
+        let err = InferError::GenerationError(error.to_string());
+        metrics::increment_counter!("tgi_request_failure", "err" => "generation");
+        tracing::error!("{err}");
+
+        // unwrap_or is valid here as we don't care if the receiver is gone.
+        entry
+            .response_tx
+            .send(Err(err))
+            .unwrap_or(());
+    });
+}
+
+#[derive(Debug)]
+pub(crate) enum InferStreamResponse {
+    // Optional first message
+    Prefill(PrefillTokens),
+    // Intermediate messages
+    Token(Token),
+    // Last message
+    End {
+        token: Token,
+        generated_text: GeneratedText,
+        start: Instant,
+        queued: Instant,
+    },
+}
+
+#[derive(Debug)]
+pub(crate) struct InferResponse {
+    pub(crate) prefill: Vec<PrefillToken>,
+    pub(crate) tokens: Vec<Token>,
+    pub(crate) generated_text: GeneratedText,
+    pub(crate) queued: Instant,
+    pub(crate) start: Instant,
+}
+
+#[derive(Debug, Error)]
+pub enum InferError {
+    #[error("Request failed during generation: {0}")]
+    GenerationError(String),
+    #[error("Model is overloaded")]
+    Overloaded(#[from] TryAcquireError),
+    #[error("Input validation error: {0}")]
+    ValidationError(#[from] ValidationError),
+    #[error("Incomplete generation")]
+    IncompleteGeneration,
+}
+
+impl InferError {
+    pub(crate) fn error_type(&self) -> &str {
+        match self {
+            InferError::GenerationError(_) => "generation",
+            InferError::Overloaded(_) => "overloaded",
+            InferError::ValidationError(_) => "validation",
+            InferError::IncompleteGeneration => "incomplete_generation",
+        }
+    }
+}
diff --git a/router/src/lib.rs b/router/src/lib.rs
new file mode 100644
index 0000000000000000000000000000000000000000..080dc4f4e66edefed579f3b73c25a2cbbbe40d2d
--- /dev/null
+++ b/router/src/lib.rs
@@ -0,0 +1,301 @@
+mod health;
+/// Text Generation Inference Webserver
+mod infer;
+mod queue;
+pub mod server;
+mod validation;
+
+use infer::Infer;
+use queue::{Entry, Queue};
+use serde::{Deserialize, Serialize};
+use utoipa::ToSchema;
+use validation::Validation;
+
+/// Hub type
+#[derive(Clone, Debug, Deserialize)]
+pub struct HubModelInfo {
+    #[serde(rename(deserialize = "id"))]
+    pub model_id: String,
+    pub sha: Option<String>,
+    pub pipeline_tag: Option<String>,
+}
+
+#[derive(Clone, Debug, Serialize, ToSchema)]
+pub struct Info {
+    /// Model info
+    #[schema(example = "bigscience/blomm-560m")]
+    pub model_id: String,
+    #[schema(nullable = true, example = "e985a63cdc139290c5f700ff1929f0b5942cced2")]
+    pub model_sha: Option<String>,
+    #[schema(example = "torch.float16")]
+    pub model_dtype: String,
+    #[schema(example = "cuda")]
+    pub model_device_type: String,
+    #[schema(nullable = true, example = "text-generation")]
+    pub model_pipeline_tag: Option<String>,
+    /// Router Parameters
+    #[schema(example = "128")]
+    pub max_concurrent_requests: usize,
+    #[schema(example = "2")]
+    pub max_best_of: usize,
+    #[schema(example = "4")]
+    pub max_stop_sequences: usize,
+    #[schema(example = "1024")]
+    pub max_input_length: usize,
+    #[schema(example = "2048")]
+    pub max_total_tokens: usize,
+    #[schema(example = "1.2")]
+    pub waiting_served_ratio: f32,
+    #[schema(example = "32000")]
+    pub max_batch_total_tokens: u32,
+    #[schema(example = "20")]
+    pub max_waiting_tokens: usize,
+    #[schema(example = "2")]
+    pub validation_workers: usize,
+    /// Router Info
+    #[schema(example = "0.5.0")]
+    pub version: &'static str,
+    #[schema(nullable = true, example = "null")]
+    pub sha: Option<&'static str>,
+    #[schema(nullable = true, example = "null")]
+    pub docker_label: Option<&'static str>,
+}
+
+#[derive(Clone, Debug, Deserialize, ToSchema)]
+pub(crate) struct GenerateParameters {
+    #[serde(default)]
+    #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 1)]
+    pub best_of: Option<usize>,
+    #[serde(default)]
+    #[schema(
+        exclusive_minimum = 0.0,
+        nullable = true,
+        default = "null",
+        example = 0.5
+    )]
+    pub temperature: Option<f32>,
+    #[serde(default)]
+    #[schema(
+        exclusive_minimum = 0.0,
+        nullable = true,
+        default = "null",
+        example = 1.03
+    )]
+    pub repetition_penalty: Option<f32>,
+    #[serde(default)]
+    #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 10)]
+    pub top_k: Option<i32>,
+    #[serde(default)]
+    #[schema(
+        exclusive_minimum = 0.0,
+        maximum = 1.0,
+        nullable = true,
+        default = "null",
+        example = 0.95
+    )]
+    pub top_p: Option<f32>,
+    #[serde(default)]
+    #[schema(
+        exclusive_minimum = 0.0,
+        maximum = 1.0,
+        nullable = true,
+        default = "null",
+        example = 0.95
+    )]
+    pub typical_p: Option<f32>,
+    #[serde(default)]
+    #[schema(default = "false", example = true)]
+    pub do_sample: bool,
+    #[serde(default = "default_max_new_tokens")]
+    #[schema(exclusive_minimum = 0, exclusive_maximum = 512, default = "20")]
+    pub max_new_tokens: u32,
+    #[serde(default)]
+    #[schema(nullable = true, default = "null", example = false)]
+    pub return_full_text: Option<bool>,
+    #[serde(default)]
+    #[schema(inline, max_items = 4, example = json ! (["photographer"]))]
+    pub stop: Vec<String>,
+    #[serde(default)]
+    #[schema(nullable = true, default = "null", example = "null")]
+    pub truncate: Option<usize>,
+    #[serde(default)]
+    #[schema(default = "false", example = true)]
+    pub watermark: bool,
+    #[serde(default)]
+    #[schema(default = "true")]
+    pub details: bool,
+    #[serde(default)]
+    #[schema(
+        exclusive_minimum = 0,
+        nullable = true,
+        default = "null",
+        example = "null"
+    )]
+    pub seed: Option<u64>,
+}
+
+fn default_max_new_tokens() -> u32 {
+    20
+}
+
+fn default_parameters() -> GenerateParameters {
+    GenerateParameters {
+        best_of: None,
+        temperature: None,
+        repetition_penalty: None,
+        top_k: None,
+        top_p: None,
+        typical_p: None,
+        do_sample: false,
+        max_new_tokens: default_max_new_tokens(),
+        return_full_text: None,
+        stop: Vec::new(),
+        truncate: None,
+        watermark: false,
+        details: false,
+        seed: None,
+    }
+}
+
+#[derive(Clone, Debug, Deserialize, ToSchema)]
+pub(crate) struct GenerateRequest {
+    #[schema(example = "My name is Olivier and I")]
+    pub inputs: String,
+    #[serde(default = "default_parameters")]
+    pub parameters: GenerateParameters,
+}
+
+#[derive(Clone, Debug, Deserialize, ToSchema)]
+pub(crate) struct CompatGenerateRequest {
+    #[schema(example = "My name is Olivier and I")]
+    pub inputs: String,
+    #[serde(default = "default_parameters")]
+    pub parameters: GenerateParameters,
+    #[serde(default)]
+    #[allow(dead_code)]
+    pub stream: bool,
+}
+
+impl From<CompatGenerateRequest> for GenerateRequest {
+    fn from(req: CompatGenerateRequest) -> Self {
+        Self {
+            inputs: req.inputs,
+            parameters: req.parameters,
+        }
+    }
+}
+
+#[derive(Debug, Serialize, ToSchema)]
+pub struct PrefillToken {
+    #[schema(example = 0)]
+    id: u32,
+    #[schema(example = "test")]
+    text: String,
+    #[schema(nullable = true, example = - 0.34)]
+    logprob: f32,
+}
+
+#[derive(Debug, Serialize, ToSchema)]
+pub struct Token {
+    #[schema(example = 0)]
+    id: u32,
+    #[schema(example = "test")]
+    text: String,
+    #[schema(nullable = true, example = - 0.34)]
+    logprob: f32,
+    #[schema(example = "false")]
+    special: bool,
+}
+
+#[derive(Serialize, ToSchema)]
+#[serde(rename_all(serialize = "snake_case"))]
+pub(crate) enum FinishReason {
+    #[schema(rename = "length")]
+    Length,
+    #[serde(rename = "eos_token")]
+    #[schema(rename = "eos_token")]
+    EndOfSequenceToken,
+    #[schema(rename = "stop_sequence")]
+    StopSequence,
+}
+
+#[derive(Serialize, ToSchema)]
+pub(crate) struct BestOfSequence {
+    #[schema(example = "test")]
+    pub generated_text: String,
+    #[schema(example = "length")]
+    pub finish_reason: FinishReason,
+    #[schema(example = 1)]
+    pub generated_tokens: u32,
+    #[schema(nullable = true, example = 42)]
+    pub seed: Option<u64>,
+    pub prefill: Vec<PrefillToken>,
+    pub tokens: Vec<Token>,
+}
+
+#[derive(Serialize, ToSchema)]
+pub(crate) struct Details {
+    #[schema(example = "length")]
+    pub finish_reason: FinishReason,
+    #[schema(example = 1)]
+    pub generated_tokens: u32,
+    #[schema(nullable = true, example = 42)]
+    pub seed: Option<u64>,
+    pub prefill: Vec<PrefillToken>,
+    pub tokens: Vec<Token>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub best_of_sequences: Option<Vec<BestOfSequence>>,
+}
+
+#[derive(Serialize, ToSchema)]
+pub(crate) struct GenerateResponse {
+    #[schema(example = "test")]
+    pub generated_text: String,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub details: Option<Details>,
+}
+
+#[derive(Serialize, ToSchema)]
+pub(crate) struct StreamDetails {
+    #[schema(example = "length")]
+    pub finish_reason: FinishReason,
+    #[schema(example = 1)]
+    pub generated_tokens: u32,
+    #[schema(nullable = true, example = 42)]
+    pub seed: Option<u64>,
+}
+
+#[derive(Serialize, ToSchema)]
+pub(crate) struct StreamResponse {
+    pub token: Token,
+    #[schema(nullable = true, default = "null", example = "test")]
+    pub generated_text: Option<String>,
+    #[schema(nullable = true, default = "null")]
+    pub details: Option<StreamDetails>,
+}
+
+#[derive(Serialize, ToSchema)]
+pub(crate) struct ErrorResponse {
+    pub error: String,
+    pub error_type: String,
+}
+
+#[cfg(test)]
+mod tests {
+    use std::io::Write;
+    use tokenizers::Tokenizer;
+
+    pub(crate) async fn get_tokenizer() -> Tokenizer {
+        if !std::path::Path::new("tokenizer.json").exists() {
+            let content = reqwest::get("https://huggingface.co/gpt2/raw/main/tokenizer.json")
+                .await
+                .unwrap()
+                .bytes()
+                .await
+                .unwrap();
+            let mut file = std::fs::File::create("tokenizer.json").unwrap();
+            file.write_all(&content).unwrap();
+        }
+        Tokenizer::from_file("tokenizer.json").unwrap()
+    }
+}
diff --git a/router/src/main.rs b/router/src/main.rs
new file mode 100644
index 0000000000000000000000000000000000000000..28db607118c1f35bc082a729377a5006c289f308
--- /dev/null
+++ b/router/src/main.rs
@@ -0,0 +1,276 @@
+/// Text Generation Inference webserver entrypoint
+use axum::http::HeaderValue;
+use clap::Parser;
+use opentelemetry::sdk::propagation::TraceContextPropagator;
+use opentelemetry::sdk::trace;
+use opentelemetry::sdk::trace::Sampler;
+use opentelemetry::sdk::Resource;
+use opentelemetry::{global, KeyValue};
+use opentelemetry_otlp::WithExportConfig;
+use std::net::{IpAddr, Ipv4Addr, SocketAddr};
+use std::path::Path;
+use text_generation_client::ShardedClient;
+use text_generation_router::{server, HubModelInfo};
+use tokenizers::{FromPretrainedParameters, Tokenizer};
+use tower_http::cors::AllowOrigin;
+use tracing_subscriber::layer::SubscriberExt;
+use tracing_subscriber::util::SubscriberInitExt;
+use tracing_subscriber::{EnvFilter, Layer};
+
+/// App Configuration
+#[derive(Parser, Debug)]
+#[clap(author, version, about, long_about = None)]
+struct Args {
+    #[clap(default_value = "128", long, env)]
+    max_concurrent_requests: usize,
+    #[clap(default_value = "2", long, env)]
+    max_best_of: usize,
+    #[clap(default_value = "4", long, env)]
+    max_stop_sequences: usize,
+    #[clap(default_value = "1000", long, env)]
+    max_input_length: usize,
+    #[clap(default_value = "1512", long, env)]
+    max_total_tokens: usize,
+    #[clap(long, env)]
+    max_batch_size: Option<usize>,
+    #[clap(default_value = "1.2", long, env)]
+    waiting_served_ratio: f32,
+    #[clap(default_value = "32000", long, env)]
+    max_batch_total_tokens: u32,
+    #[clap(default_value = "20", long, env)]
+    max_waiting_tokens: usize,
+    #[clap(default_value = "3000", long, short, env)]
+    port: u16,
+    #[clap(default_value = "/tmp/text-generation-server-0", long, env)]
+    master_shard_uds_path: String,
+    #[clap(default_value = "bigscience/bloom", long, env)]
+    tokenizer_name: String,
+    #[clap(default_value = "main", long, env)]
+    revision: String,
+    #[clap(default_value = "2", long, env)]
+    validation_workers: usize,
+    #[clap(long, env)]
+    json_output: bool,
+    #[clap(long, env)]
+    otlp_endpoint: Option<String>,
+    #[clap(long, env)]
+    cors_allow_origin: Option<Vec<String>>,
+}
+
+fn main() -> Result<(), std::io::Error> {
+    // Get args
+    let args = Args::parse();
+    // Pattern match configuration
+    let Args {
+        max_concurrent_requests,
+        max_best_of,
+        max_stop_sequences,
+        max_input_length,
+        max_total_tokens,
+        max_batch_size,
+        waiting_served_ratio,
+        mut max_batch_total_tokens,
+        max_waiting_tokens,
+        port,
+        master_shard_uds_path,
+        tokenizer_name,
+        revision,
+        validation_workers,
+        json_output,
+        otlp_endpoint,
+        cors_allow_origin,
+    } = args;
+
+    if validation_workers == 0 {
+        panic!("validation_workers must be > 0");
+    }
+
+    // CORS allowed origins
+    // map to go inside the option and then map to parse from String to HeaderValue
+    // Finally, convert to AllowOrigin
+    let cors_allow_origin: Option<AllowOrigin> = cors_allow_origin.map(|cors_allow_origin| {
+        AllowOrigin::list(
+            cors_allow_origin
+                .iter()
+                .map(|origin| origin.parse::<HeaderValue>().unwrap()),
+        )
+    });
+
+    // Parse Huggingface hub token
+    let authorization_token = std::env::var("HUGGING_FACE_HUB_TOKEN").ok();
+
+    // Tokenizer instance
+    // This will only be used to validate payloads
+    let local_path = Path::new(&tokenizer_name);
+    let local_model = local_path.exists() && local_path.is_dir();
+    let tokenizer = if local_model {
+        // Load local tokenizer
+        Tokenizer::from_file(local_path.join("tokenizer.json")).ok()
+    } else {
+        // Download and instantiate tokenizer
+        // We need to download it outside of the Tokio runtime
+        let params = FromPretrainedParameters {
+            revision: revision.clone(),
+            auth_token: authorization_token.clone(),
+            ..Default::default()
+        };
+        Tokenizer::from_pretrained(tokenizer_name.clone(), Some(params)).ok()
+    };
+
+    // Launch Tokio runtime
+    tokio::runtime::Builder::new_multi_thread()
+        .enable_all()
+        .build()
+        .unwrap()
+        .block_on(async {
+            init_logging(otlp_endpoint, json_output);
+
+            if let Some(max_batch_size) = max_batch_size{
+                tracing::warn!("`max-batch-size` is deprecated. Use `max-batch-total-tokens` instead");
+                max_batch_total_tokens = (max_batch_size * max_total_tokens) as u32;
+                tracing::warn!("Overriding `max-batch-total-tokens` value with `max-batch-size` * `max-total-tokens` = {max_batch_total_tokens}");
+            }
+
+            if tokenizer.is_none() {
+                tracing::warn!(
+                    "Could not find a fast tokenizer implementation for {tokenizer_name}"
+                );
+                tracing::warn!("Rust input length validation and truncation is disabled");
+            }
+
+            // Get Model info
+            let model_info = match local_model {
+                true => HubModelInfo {
+                    model_id: tokenizer_name.clone(),
+                    sha: None,
+                    pipeline_tag: None,
+                },
+                false => get_model_info(&tokenizer_name, &revision, authorization_token).await,
+            };
+
+            // if pipeline-tag == text-generation we default to return_full_text = true
+            let compat_return_full_text = match &model_info.pipeline_tag {
+                None => {
+                    tracing::warn!("no pipeline tag found for model {tokenizer_name}");
+                    false
+                }
+                Some(pipeline_tag) => pipeline_tag.as_str() == "text-generation",
+            };
+
+            // Instantiate sharded client from the master unix socket
+            let mut sharded_client = ShardedClient::connect_uds(master_shard_uds_path)
+                .await
+                .expect("Could not connect to server");
+            // Clear the cache; useful if the webserver rebooted
+            sharded_client
+                .clear_cache(None)
+                .await
+                .expect("Unable to clear cache");
+            // Get info from the shard
+            let shard_info = sharded_client
+                .info()
+                .await
+                .expect("Unable to get shard info");
+            tracing::info!("Connected");
+
+            // Binds on localhost
+            let addr = SocketAddr::new(IpAddr::V4(Ipv4Addr::new(0, 0, 0, 0)), port);
+
+            // Run server
+            server::run(
+                model_info,
+                shard_info,
+                compat_return_full_text,
+                max_concurrent_requests,
+                max_best_of,
+                max_stop_sequences,
+                max_input_length,
+                max_total_tokens,
+                waiting_served_ratio,
+                max_batch_total_tokens,
+                max_waiting_tokens,
+                sharded_client,
+                tokenizer,
+                validation_workers,
+                addr,
+                cors_allow_origin,
+            )
+            .await;
+            Ok(())
+        })
+}
+
+/// Init logging using env variables LOG_LEVEL and LOG_FORMAT:
+///     - otlp_endpoint is an optional URL to an Open Telemetry collector
+///     - LOG_LEVEL may be TRACE, DEBUG, INFO, WARN or ERROR (default to INFO)
+///     - LOG_FORMAT may be TEXT or JSON (default to TEXT)
+fn init_logging(otlp_endpoint: Option<String>, json_output: bool) {
+    let mut layers = Vec::new();
+
+    // STDOUT/STDERR layer
+    let fmt_layer = tracing_subscriber::fmt::layer()
+        .with_file(true)
+        .with_line_number(true);
+
+    let fmt_layer = match json_output {
+        true => fmt_layer.json().flatten_event(true).boxed(),
+        false => fmt_layer.boxed(),
+    };
+    layers.push(fmt_layer);
+
+    // OpenTelemetry tracing layer
+    if let Some(otlp_endpoint) = otlp_endpoint {
+        global::set_text_map_propagator(TraceContextPropagator::new());
+
+        let tracer = opentelemetry_otlp::new_pipeline()
+            .tracing()
+            .with_exporter(
+                opentelemetry_otlp::new_exporter()
+                    .tonic()
+                    .with_endpoint(otlp_endpoint),
+            )
+            .with_trace_config(
+                trace::config()
+                    .with_resource(Resource::new(vec![KeyValue::new(
+                        "service.name",
+                        "text-generation-inference.router",
+                    )]))
+                    .with_sampler(Sampler::AlwaysOn),
+            )
+            .install_batch(opentelemetry::runtime::Tokio);
+
+        if let Ok(tracer) = tracer {
+            layers.push(tracing_opentelemetry::layer().with_tracer(tracer).boxed());
+            axum_tracing_opentelemetry::init_propagator().unwrap();
+        };
+    }
+
+    // Filter events with LOG_LEVEL
+    let env_filter =
+        EnvFilter::try_from_env("LOG_LEVEL").unwrap_or_else(|_| EnvFilter::new("info"));
+
+    tracing_subscriber::registry()
+        .with(env_filter)
+        .with(layers)
+        .init();
+}
+
+/// get model info from the Huggingface Hub
+pub async fn get_model_info(model_id: &str, revision: &str, token: Option<String>) -> HubModelInfo {
+    let client = reqwest::Client::new();
+    let mut builder = client.get(format!(
+        "https://huggingface.co/api/models/{model_id}/revision/{revision}"
+    ));
+    if let Some(token) = token {
+        builder = builder.bearer_auth(token);
+    }
+
+    let model_info = builder
+        .send()
+        .await
+        .expect("Could not connect to hf.co")
+        .text()
+        .await
+        .expect("error when retrieving model info from hf.co");
+    serde_json::from_str(&model_info).expect("unable to parse model info")
+}
diff --git a/router/src/queue.rs b/router/src/queue.rs
new file mode 100644
index 0000000000000000000000000000000000000000..94851e1c5995c5e64f8ce9580f01a94c94f8c61f
--- /dev/null
+++ b/router/src/queue.rs
@@ -0,0 +1,479 @@
+use crate::infer::InferError;
+use crate::infer::InferStreamResponse;
+use crate::validation::ValidGenerateRequest;
+use nohash_hasher::{BuildNoHashHasher, IntMap};
+use std::collections::VecDeque;
+use text_generation_client::{Batch, Request};
+use tokio::sync::oneshot;
+use tokio::time::Instant;
+use tracing::{info_span, instrument, Span};
+
+/// Queue entry
+#[derive(Debug)]
+pub(crate) struct Entry {
+    /// Request
+    pub request: ValidGenerateRequest,
+    /// Response sender to communicate between the Infer struct and the batching_task
+    pub response_tx: flume::Sender<Result<InferStreamResponse, InferError>>,
+    /// Span that will live as long as entry
+    pub span: Span,
+    /// Temporary span used as a guard when logging inference, wait times...
+    pub temp_span: Option<Span>,
+    /// Instant when this entry was queued
+    pub queue_time: Instant,
+    /// Instant when this entry was added to a batch
+    pub batch_time: Option<Instant>,
+}
+
+/// Request Queue
+#[derive(Debug, Clone)]
+pub(crate) struct Queue {
+    /// Channel to communicate with the background queue task
+    queue_sender: flume::Sender<QueueCommand>,
+}
+
+impl Queue {
+    pub(crate) fn new(requires_padding: bool) -> Self {
+        // Create channel
+        let (queue_sender, queue_receiver) = flume::unbounded();
+
+        // Launch background queue task
+        tokio::spawn(queue_task(requires_padding, queue_receiver));
+
+        Self { queue_sender }
+    }
+
+    /// Append an entry to the queue
+    #[instrument(skip_all)]
+    pub(crate) fn append(&self, entry: Entry) {
+        // Send append command to the background task managing the state
+        // Unwrap is safe here
+        self.queue_sender
+            .send(QueueCommand::Append(entry, Span::current()))
+            .unwrap();
+    }
+
+    // Get the next batch
+    #[instrument(skip(self))]
+    pub(crate) async fn next_batch(
+        &self,
+        min_size: Option<usize>,
+        token_budget: u32,
+    ) -> Option<NextBatch> {
+        // Create response channel
+        let (response_sender, response_receiver) = oneshot::channel();
+        // Send next batch command to the background task managing the state
+        // Unwrap is safe here
+        self.queue_sender
+            .send(QueueCommand::NextBatch {
+                min_size,
+                token_budget,
+                response_sender,
+                span: Span::current(),
+            })
+            .unwrap();
+        // Await on response channel
+        // Unwrap is safe here
+        response_receiver.await.unwrap()
+    }
+}
+
+// Background task responsible of the queue state
+async fn queue_task(requires_padding: bool, receiver: flume::Receiver<QueueCommand>) {
+    let mut state = State::new(requires_padding);
+
+    while let Ok(cmd) = receiver.recv_async().await {
+        match cmd {
+            QueueCommand::Append(entry, span) => {
+                span.in_scope(|| state.append(entry));
+                metrics::increment_gauge!("tgi_queue_size", 1.0);
+            }
+            QueueCommand::NextBatch {
+                min_size,
+                token_budget,
+                response_sender,
+                span,
+            } => span.in_scope(|| {
+                let next_batch = state.next_batch(min_size, token_budget);
+                response_sender.send(next_batch).unwrap_or(());
+                metrics::gauge!("tgi_queue_size", state.entries.len() as f64);
+            }),
+        }
+    }
+}
+
+/// Queue State
+#[derive(Debug)]
+struct State {
+    /// Queue entries organized in a Vec
+    entries: VecDeque<(u64, Entry)>,
+
+    /// Id of the next entry
+    next_id: u64,
+
+    /// Id of the next batch
+    next_batch_id: u64,
+
+    /// Whether the model is using padding
+    requires_padding: bool,
+}
+
+impl State {
+    fn new(requires_padding: bool) -> Self {
+        Self {
+            entries: VecDeque::with_capacity(128),
+            next_id: 0,
+            next_batch_id: 0,
+            requires_padding,
+        }
+    }
+
+    /// Append an entry to the queue
+    fn append(&mut self, mut entry: Entry) {
+        // Create a span that will live as long as the entry is in the queue waiting to be batched
+        let queue_span = info_span!(parent: &entry.span, "queued");
+        entry.temp_span = Some(queue_span);
+
+        // Push entry in the queue
+        self.entries.push_back((self.next_id, entry));
+        self.next_id += 1;
+    }
+
+    // Get the next batch
+    fn next_batch(&mut self, min_size: Option<usize>, token_budget: u32) -> Option<NextBatch> {
+        if self.entries.is_empty() {
+            return None;
+        }
+
+        // Check if we have enough entries
+        if let Some(min_size) = min_size {
+            if self.entries.len() < min_size {
+                return None;
+            }
+        }
+
+        // Create span for this batch to add context to inference calls
+        let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty);
+        next_batch_span.follows_from(&Span::current());
+
+        let mut batch_requests = Vec::with_capacity(self.entries.len());
+        let mut batch_entries =
+            IntMap::with_capacity_and_hasher(self.entries.len(), BuildNoHashHasher::default());
+
+        let mut max_input_length = 0;
+        let mut prefill_tokens: u32 = 0;
+        let mut decode_tokens: u32 = 0;
+
+        // Pop entries starting from the front of the queue
+        while let Some((id, mut entry)) = self.entries.pop_front() {
+            // Filter entries where the response receiver was dropped (== entries where the request
+            // was dropped by the client)
+            if entry.response_tx.is_disconnected() {
+                metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
+                continue;
+            }
+
+            if self.requires_padding {
+                // We pad to max input length in the Python shards
+                // We need to take these padding tokens into the equation
+                max_input_length = max_input_length.max(entry.request.input_length);
+                prefill_tokens = (batch_requests.len() + 1) as u32 * max_input_length
+            } else {
+                prefill_tokens += entry.request.input_length;
+            }
+
+            decode_tokens += entry.request.stopping_parameters.max_new_tokens;
+
+            if (prefill_tokens + decode_tokens) > token_budget {
+                // Entry is over budget
+                // Add it back to the front
+                self.entries.push_front((id, entry));
+                break;
+            }
+
+            // Create a new span to link the batch back to this entry
+            let entry_batch_span = info_span!(parent: &entry.span, "infer");
+            // Add relationships
+            next_batch_span.follows_from(&entry_batch_span);
+            entry_batch_span.follows_from(&next_batch_span);
+            // Update entry
+            entry.temp_span = Some(entry_batch_span);
+
+            batch_requests.push(Request {
+                id,
+                inputs: entry.request.inputs.clone(),
+                truncate: entry.request.truncate,
+                parameters: Some(entry.request.parameters.clone()),
+                stopping_parameters: Some(entry.request.stopping_parameters.clone()),
+            });
+            // Set batch_time
+            entry.batch_time = Some(Instant::now());
+            // Insert in batch_entries IntMap
+            batch_entries.insert(id, entry);
+        }
+
+        // Empty batch
+        if batch_requests.is_empty() {
+            return None;
+        }
+
+        // Check if our batch is big enough
+        if let Some(min_size) = min_size {
+            // Batch is too small
+            if batch_requests.len() < min_size {
+                // Add back entries to the queue in the correct order
+                for r in batch_requests.into_iter().rev() {
+                    let id = r.id;
+                    let entry = batch_entries.remove(&id).unwrap();
+                    self.entries.push_front((id, entry));
+                }
+
+                return None;
+            }
+        }
+
+        // Final batch size
+        let size = batch_requests.len() as u32;
+        next_batch_span.record("batch_size", size);
+
+        let batch = Batch {
+            id: self.next_batch_id,
+            requests: batch_requests,
+            size,
+            max_tokens: (prefill_tokens + decode_tokens),
+        };
+        // Increment batch id
+        self.next_batch_id += 1;
+
+        metrics::histogram!("tgi_batch_next_size", batch.size as f64);
+
+        Some((batch_entries, batch, next_batch_span))
+    }
+}
+
+type NextBatch = (IntMap<u64, Entry>, Batch, Span);
+
+#[derive(Debug)]
+enum QueueCommand {
+    Append(Entry, Span),
+    NextBatch {
+        min_size: Option<usize>,
+        token_budget: u32,
+        response_sender: oneshot::Sender<Option<NextBatch>>,
+        span: Span,
+    },
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
+    use tracing::info_span;
+
+    fn default_entry() -> (
+        Entry,
+        flume::Receiver<Result<InferStreamResponse, InferError>>,
+    ) {
+        let (response_tx, receiver_tx) = flume::unbounded();
+
+        let entry = Entry {
+            request: ValidGenerateRequest {
+                inputs: "".to_string(),
+                input_length: 0,
+                truncate: 0,
+                parameters: NextTokenChooserParameters {
+                    temperature: 0.0,
+                    top_k: 0,
+                    top_p: 0.0,
+                    typical_p: 0.0,
+                    do_sample: false,
+                    seed: 0,
+                    repetition_penalty: 0.0,
+                    watermark: false,
+                },
+                stopping_parameters: StoppingCriteriaParameters {
+                    ignore_eos_token: false,
+                    max_new_tokens: 1,
+                    stop_sequences: vec![],
+                },
+            },
+            response_tx,
+            span: info_span!("entry"),
+            temp_span: None,
+            queue_time: Instant::now(),
+            batch_time: None,
+        };
+        (entry, receiver_tx)
+    }
+
+    #[test]
+    fn test_append() {
+        let mut state = State::new(false);
+        let (entry, _guard) = default_entry();
+
+        assert_eq!(state.next_id, 0);
+        assert_eq!(state.entries.len(), 0);
+
+        state.append(entry);
+
+        assert_eq!(state.next_id, 1);
+        assert_eq!(state.entries.len(), 1);
+        let (id, _) = state.entries.remove(0).unwrap();
+        assert_eq!(id, 0);
+    }
+
+    #[test]
+    fn test_next_batch_empty() {
+        let mut state = State::new(false);
+
+        assert!(state.next_batch(None, 1).is_none());
+        assert!(state.next_batch(Some(1), 1).is_none());
+    }
+
+    #[test]
+    fn test_next_batch_min_size() {
+        let mut state = State::new(false);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        state.append(entry1);
+        state.append(entry2);
+
+        let (entries, batch, _) = state.next_batch(None, 2).unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&0));
+        assert!(entries.contains_key(&1));
+        assert!(entries.get(&0).unwrap().batch_time.is_some());
+        assert!(entries.get(&1).unwrap().batch_time.is_some());
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 2);
+
+        assert_eq!(state.next_id, 2);
+        assert_eq!(state.entries.len(), 0);
+        assert_eq!(state.next_batch_id, 1);
+
+        let (entry3, _guard3) = default_entry();
+        state.append(entry3);
+
+        assert!(state.next_batch(Some(2), 2).is_none());
+
+        assert_eq!(state.next_id, 3);
+        assert_eq!(state.entries.len(), 1);
+        let (id, _) = state.entries.remove(0).unwrap();
+        assert_eq!(id, 2);
+    }
+
+    #[test]
+    fn test_next_batch_token_budget() {
+        let mut state = State::new(false);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        state.append(entry1);
+        state.append(entry2);
+
+        let (entries, batch, _) = state.next_batch(None, 1).unwrap();
+        assert_eq!(entries.len(), 1);
+        assert!(entries.contains_key(&0));
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 1);
+
+        assert_eq!(state.next_id, 2);
+        assert_eq!(state.entries.len(), 1);
+        assert_eq!(state.next_batch_id, 1);
+
+        let (entry3, _guard3) = default_entry();
+        state.append(entry3);
+
+        let (entries, batch, _) = state.next_batch(None, 3).unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&1));
+        assert!(entries.contains_key(&2));
+        assert_eq!(batch.id, 1);
+        assert_eq!(batch.size, 2);
+
+        assert_eq!(state.next_id, 3);
+        assert_eq!(state.entries.len(), 0);
+        assert_eq!(state.next_batch_id, 2);
+    }
+
+    #[tokio::test]
+    async fn test_queue_append() {
+        let queue = Queue::new(false);
+        let (entry, _guard) = default_entry();
+        queue.append(entry);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_empty() {
+        let queue = Queue::new(false);
+
+        assert!(queue.next_batch(None, 1).await.is_none());
+        assert!(queue.next_batch(Some(1), 1).await.is_none());
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_min_size() {
+        let queue = Queue::new(false);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        queue.append(entry1);
+        queue.append(entry2);
+
+        let (entries, batch, _) = queue.next_batch(None, 2).await.unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&0));
+        assert!(entries.contains_key(&1));
+        assert!(entries.get(&0).unwrap().batch_time.is_some());
+        assert!(entries.get(&1).unwrap().batch_time.is_some());
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 2);
+
+        let (entry3, _guard3) = default_entry();
+        queue.append(entry3);
+
+        // Not enough requests pending
+        assert!(queue.next_batch(Some(2), 2).await.is_none());
+        // Not enough token budget
+        assert!(queue.next_batch(Some(1), 0).await.is_none());
+        // Ok
+        let (entries2, batch2, _) = queue.next_batch(Some(1), 2).await.unwrap();
+        assert_eq!(entries2.len(), 1);
+        assert!(entries2.contains_key(&2));
+        assert!(entries2.get(&2).unwrap().batch_time.is_some());
+        assert_eq!(batch2.id, 1);
+        assert_eq!(batch2.size, 1);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_token_budget() {
+        let queue = Queue::new(false);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        queue.append(entry1);
+        queue.append(entry2);
+
+        let (entries, batch, _) = queue.next_batch(None, 1).await.unwrap();
+        assert_eq!(entries.len(), 1);
+        assert!(entries.contains_key(&0));
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 1);
+
+        let (entry3, _guard3) = default_entry();
+        queue.append(entry3);
+
+        let (entries, batch, _) = queue.next_batch(None, 3).await.unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&1));
+        assert!(entries.contains_key(&2));
+        assert_eq!(batch.id, 1);
+        assert_eq!(batch.size, 2);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_dropped_receiver() {
+        let queue = Queue::new(false);
+        let (entry, _) = default_entry();
+        queue.append(entry);
+
+        assert!(queue.next_batch(None, 1).await.is_none());
+    }
+}
diff --git a/router/src/server.rs b/router/src/server.rs
new file mode 100644
index 0000000000000000000000000000000000000000..c5fc41efc72644c882c0eb820654ee132db093d0
--- /dev/null
+++ b/router/src/server.rs
@@ -0,0 +1,744 @@
+use crate::health::Health;
+/// HTTP Server logic
+use crate::infer::{InferError, InferResponse, InferStreamResponse};
+use crate::validation::ValidationError;
+use crate::{
+    BestOfSequence, CompatGenerateRequest, Details, ErrorResponse, FinishReason,
+    GenerateParameters, GenerateRequest, GenerateResponse, HubModelInfo, Infer, Info, PrefillToken,
+    StreamDetails, StreamResponse, Token, Validation,
+};
+use axum::extract::Extension;
+use axum::http::{HeaderMap, Method, StatusCode};
+use axum::response::sse::{Event, KeepAlive, Sse};
+use axum::response::{IntoResponse, Response};
+use axum::routing::{get, post};
+use axum::{http, Json, Router};
+use axum_tracing_opentelemetry::opentelemetry_tracing_layer;
+use futures::stream::StreamExt;
+use futures::Stream;
+use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
+use std::convert::Infallible;
+use std::net::SocketAddr;
+use std::sync::atomic::AtomicBool;
+use std::sync::Arc;
+use text_generation_client::{ShardInfo, ShardedClient};
+use tokenizers::Tokenizer;
+use tokio::signal;
+use tokio::time::Instant;
+use tower_http::cors::{AllowOrigin, CorsLayer};
+use tracing::{info_span, instrument, Instrument};
+use utoipa::OpenApi;
+use utoipa_swagger_ui::SwaggerUi;
+
+/// Generate tokens if `stream == false` or a stream of token if `stream == true`
+#[utoipa::path(
+    post,
+    tag = "Text Generation Inference",
+    path = "/",
+    request_body = CompatGenerateRequest,
+    responses(
+        (status = 200, description = "See /generate or /generate_stream"),
+        (status = 424, description = "Generation Error", body = ErrorResponse,
+            example = json ! ({"error": "Request failed during generation"})),
+        (status = 429, description = "Model is overloaded", body = ErrorResponse,
+            example = json ! ({"error": "Model is overloaded"})),
+        (status = 422, description = "Input validation error", body = ErrorResponse,
+            example = json ! ({"error": "Input validation error"})),
+        (status = 500, description = "Incomplete generation", body = ErrorResponse,
+            example = json ! ({"error": "Incomplete generation"})),
+    )
+)]
+#[instrument(skip(infer))]
+async fn compat_generate(
+    default_return_full_text: Extension<bool>,
+    infer: Extension<Infer>,
+    req: Json<CompatGenerateRequest>,
+) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+    let mut req = req.0;
+
+    // default return_full_text given the pipeline_tag
+    if req.parameters.return_full_text.is_none() {
+        req.parameters.return_full_text = Some(default_return_full_text.0)
+    }
+
+    // switch on stream
+    if req.stream {
+        Ok(generate_stream(infer, Json(req.into()))
+            .await
+            .into_response())
+    } else {
+        let (headers, generation) = generate(infer, Json(req.into())).await?;
+        // wrap generation inside a Vec to match api-inference
+        Ok((headers, Json(vec![generation.0])).into_response())
+    }
+}
+
+/// Text Generation Inference endpoint info
+#[utoipa::path(
+    get,
+    tag = "Text Generation Inference",
+    path = "/info",
+    responses((status = 200, description = "Served model info", body = Info))
+)]
+#[instrument]
+async fn get_model_info(info: Extension<Info>) -> Json<Info> {
+    Json(info.0)
+}
+
+#[utoipa::path(
+    get,
+    tag = "Text Generation Inference",
+    path = "/health",
+    responses(
+        (status = 200, description = "Everything is working fine"),
+        (status = 503, description = "Text generation inference is down", body = ErrorResponse,
+            example = json ! ({"error": "unhealthy", "error_type": "healthcheck"})),
+    )
+)]
+#[instrument(skip(health))]
+/// Health check method
+async fn health(mut health: Extension<Health>) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
+    match health.check().await {
+        true => Ok(()),
+        false => Err((
+            StatusCode::SERVICE_UNAVAILABLE,
+            Json(ErrorResponse {
+                error: "unhealthy".to_string(),
+                error_type: "healthcheck".to_string(),
+            }),
+        )),
+    }
+}
+
+/// Generate tokens
+#[utoipa::path(
+    post,
+    tag = "Text Generation Inference",
+    path = "/generate",
+    request_body = GenerateRequest,
+    responses(
+        (status = 200, description = "Generated Text", body = GenerateResponse),
+        (status = 424, description = "Generation Error", body = ErrorResponse,
+            example = json ! ({"error": "Request failed during generation"})),
+        (status = 429, description = "Model is overloaded", body = ErrorResponse,
+            example = json ! ({"error": "Model is overloaded"})),
+        (status = 422, description = "Input validation error", body = ErrorResponse,
+            example = json ! ({"error": "Input validation error"})),
+        (status = 500, description = "Incomplete generation", body = ErrorResponse,
+            example = json ! ({"error": "Incomplete generation"})),
+    )
+)]
+#[instrument(
+    skip(infer),
+    fields(
+        total_time,
+        validation_time,
+        queue_time,
+        inference_time,
+        time_per_token,
+        seed,
+    )
+)]
+async fn generate(
+    infer: Extension<Infer>,
+    req: Json<GenerateRequest>,
+) -> Result<(HeaderMap, Json<GenerateResponse>), (StatusCode, Json<ErrorResponse>)> {
+    let span = tracing::Span::current();
+    let start_time = Instant::now();
+    metrics::increment_counter!("tgi_request_count");
+
+    let compute_characters = req.0.inputs.chars().count();
+    let mut add_prompt = None;
+    if req.0.parameters.return_full_text.unwrap_or(false) {
+        add_prompt = Some(req.0.inputs.clone());
+    }
+
+    let details = req.0.parameters.details;
+
+    // Inference
+    let (response, best_of_responses) = match req.0.parameters.best_of {
+        Some(best_of) if best_of > 1 => {
+            let (response, best_of_responses) = infer.generate_best_of(req.0, best_of).await?;
+            (response, Some(best_of_responses))
+        }
+        _ => (infer.generate(req.0).await?, None),
+    };
+
+    // Token details
+    let details = match details {
+        true => {
+            // convert best_of_responses
+            let best_of_sequences = best_of_responses.map(|responses: Vec<InferResponse>| {
+                responses
+                    .into_iter()
+                    .map(|response: InferResponse| {
+                        // Add prompt if return_full_text
+                        let mut output_text = response.generated_text.text;
+                        if let Some(prompt) = &add_prompt {
+                            output_text = prompt.clone() + &output_text;
+                        }
+
+                        BestOfSequence {
+                            generated_text: output_text,
+                            finish_reason: FinishReason::from(
+                                response.generated_text.finish_reason,
+                            ),
+                            generated_tokens: response.generated_text.generated_tokens,
+                            prefill: response.prefill,
+                            tokens: response.tokens,
+                            seed: response.generated_text.seed,
+                        }
+                    })
+                    .collect()
+            });
+
+            Some(Details {
+                finish_reason: FinishReason::from(response.generated_text.finish_reason),
+                generated_tokens: response.generated_text.generated_tokens,
+                prefill: response.prefill,
+                tokens: response.tokens,
+                seed: response.generated_text.seed,
+                best_of_sequences,
+            })
+        }
+        false => None,
+    };
+
+    // Timings
+    let total_time = start_time.elapsed();
+    let validation_time = response.queued - start_time;
+    let queue_time = response.start - response.queued;
+    let inference_time = Instant::now() - response.start;
+    let time_per_token = inference_time / response.generated_text.generated_tokens;
+
+    // Tracing metadata
+    span.record("total_time", format!("{total_time:?}"));
+    span.record("validation_time", format!("{validation_time:?}"));
+    span.record("queue_time", format!("{queue_time:?}"));
+    span.record("inference_time", format!("{inference_time:?}"));
+    span.record("time_per_token", format!("{time_per_token:?}"));
+    span.record("seed", format!("{:?}", response.generated_text.seed));
+
+    // Headers
+    let mut headers = HeaderMap::new();
+    headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
+    headers.insert(
+        "x-compute-time",
+        total_time.as_millis().to_string().parse().unwrap(),
+    );
+    headers.insert(
+        "x-compute-characters",
+        compute_characters.to_string().parse().unwrap(),
+    );
+    headers.insert(
+        "x-total-time",
+        total_time.as_millis().to_string().parse().unwrap(),
+    );
+    headers.insert(
+        "x-validation-time",
+        validation_time.as_millis().to_string().parse().unwrap(),
+    );
+    headers.insert(
+        "x-queue-time",
+        queue_time.as_millis().to_string().parse().unwrap(),
+    );
+    headers.insert(
+        "x-inference-time",
+        inference_time.as_millis().to_string().parse().unwrap(),
+    );
+    headers.insert(
+        "x-time-per-token",
+        time_per_token.as_millis().to_string().parse().unwrap(),
+    );
+
+    // Metrics
+    metrics::increment_counter!("tgi_request_success");
+    metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
+    metrics::histogram!(
+        "tgi_request_validation_duration",
+        validation_time.as_secs_f64()
+    );
+    metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
+    metrics::histogram!(
+        "tgi_request_inference_duration",
+        inference_time.as_secs_f64()
+    );
+    metrics::histogram!(
+        "tgi_request_mean_time_per_token_duration",
+        time_per_token.as_secs_f64()
+    );
+    metrics::histogram!(
+        "tgi_request_generated_tokens",
+        response.generated_text.generated_tokens as f64
+    );
+
+    // Send response
+    let mut output_text = response.generated_text.text;
+    if let Some(prompt) = add_prompt {
+        output_text = prompt + &output_text;
+    }
+
+    tracing::info!("Output: {}", output_text);
+
+    let response = GenerateResponse {
+        generated_text: output_text,
+        details,
+    };
+    Ok((headers, Json(response)))
+}
+
+/// Generate a stream of token using Server-Sent Events
+#[utoipa::path(
+    post,
+    tag = "Text Generation Inference",
+    path = "/generate_stream",
+    request_body = GenerateRequest,
+    responses(
+        (status = 200, description = "Generated Text", body = StreamResponse,
+            content_type = "text/event-stream"),
+        (status = 424, description = "Generation Error", body = ErrorResponse,
+            example = json ! ({"error": "Request failed during generation"}),
+            content_type = "text/event-stream"),
+        (status = 429, description = "Model is overloaded", body = ErrorResponse,
+            example = json ! ({"error": "Model is overloaded"}),
+            content_type = "text/event-stream"),
+        (status = 422, description = "Input validation error", body = ErrorResponse,
+            example = json ! ({"error": "Input validation error"}),
+            content_type = "text/event-stream"),
+        (status = 500, description = "Incomplete generation", body = ErrorResponse,
+            example = json ! ({"error": "Incomplete generation"}),
+            content_type = "text/event-stream"),
+    )
+)]
+#[instrument(
+    skip(infer),
+    fields(
+        total_time,
+        validation_time,
+        queue_time,
+        inference_time,
+        time_per_token,
+        seed,
+    )
+)]
+async fn generate_stream(
+    infer: Extension<Infer>,
+    req: Json<GenerateRequest>,
+) -> (
+    HeaderMap,
+    Sse<impl Stream<Item = Result<Event, Infallible>>>,
+) {
+    let span = tracing::Span::current();
+    let start_time = Instant::now();
+    metrics::increment_counter!("tgi_request_count");
+
+    let compute_characters = req.0.inputs.chars().count();
+
+    let mut headers = HeaderMap::new();
+    headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
+    headers.insert(
+        "x-compute-characters",
+        compute_characters.to_string().parse().unwrap(),
+    );
+
+    let stream = async_stream::stream! {
+        // Inference
+        let mut end_reached = false;
+        let mut error = false;
+
+        let mut add_prompt = None;
+        if req.0.parameters.return_full_text.unwrap_or(false) {
+            add_prompt = Some(req.0.inputs.clone());
+        }
+        let details = req.0.parameters.details;
+
+        let best_of = req.0.parameters.best_of.unwrap_or(1);
+        if best_of == 1 {
+            match infer.generate_stream(req.0).instrument(info_span!(parent: &span, "async_stream")).await {
+                // Keep permit as long as generate_stream lives
+                Ok((_permit, mut response_stream)) => {
+                    // Server-Sent Event stream
+                    while let Some(response) = response_stream.next().await {
+                        match response {
+                            Ok(response) => {
+                                match response {
+                                    // Prefill is ignored
+                                    InferStreamResponse::Prefill(_) => {}
+                                    // Yield event for every new token
+                                    InferStreamResponse::Token(token) => {
+                                        // StreamResponse
+                                        let stream_token = StreamResponse {
+                                            token,
+                                            generated_text: None,
+                                            details: None,
+                                        };
+
+                                        yield Ok(Event::default().json_data(stream_token).unwrap())
+                                    }
+                                    // Yield event for last token and compute timings
+                                    InferStreamResponse::End {
+                                        token,
+                                        generated_text,
+                                        start,
+                                        queued,
+                                    } => {
+                                        // Token details
+                                        let details = match details {
+                                            true => Some(StreamDetails {
+                                                finish_reason: FinishReason::from(generated_text.finish_reason),
+                                                generated_tokens: generated_text.generated_tokens,
+                                                seed: generated_text.seed,
+                                            }),
+                                            false => None,
+                                        };
+
+                                        // Timings
+                                        let total_time = start_time.elapsed();
+                                        let validation_time = queued - start_time;
+                                        let queue_time = start - queued;
+                                        let inference_time = Instant::now() - start;
+                                        let time_per_token = inference_time / generated_text.generated_tokens;
+
+                                        // Tracing metadata
+                                        span.record("total_time", format!("{total_time:?}"));
+                                        span.record("validation_time", format!("{validation_time:?}"));
+                                        span.record("queue_time", format!("{queue_time:?}"));
+                                        span.record("inference_time", format!("{inference_time:?}"));
+                                        span.record("time_per_token", format!("{time_per_token:?}"));
+                                        span.record("seed", format!("{:?}", generated_text.seed));
+
+                                        // Metrics
+                                        metrics::increment_counter!("tgi_request_success");
+                                        metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
+                                        metrics::histogram!("tgi_request_validation_duration", validation_time.as_secs_f64());
+                                        metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
+                                        metrics::histogram!("tgi_request_inference_duration", inference_time.as_secs_f64());
+                                        metrics::histogram!("tgi_request_mean_time_per_token_duration", time_per_token.as_secs_f64());
+                                        metrics::histogram!("tgi_request_generated_tokens", generated_text.generated_tokens as f64);
+
+                                        // StreamResponse
+                                        end_reached = true;
+
+                                        let mut output_text = generated_text.text;
+                                        if let Some(prompt) = add_prompt {
+                                            output_text = prompt + &output_text;
+                                        }
+
+                                        tracing::info!(parent: &span, "Output: {}", output_text);
+
+                                        let stream_token = StreamResponse {
+                                            token,
+                                            generated_text: Some(output_text),
+                                            details
+                                        };
+
+                                        yield Ok(Event::default().json_data(stream_token).unwrap());
+                                        break;
+                                    }
+                                }
+                            }
+                            // yield error
+                            Err(err) => {
+                                error = true;
+                                yield Ok(Event::from(err));
+                                break;
+                            }
+                        }
+                    }
+                },
+                // yield error
+                Err(err) => {
+                    error = true;
+                    yield Ok(Event::from(err));
+                }
+            }
+            // Check if generation reached the end
+            // Skip if we already sent an error
+            if !end_reached && !error {
+                let err = InferError::IncompleteGeneration;
+                metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
+                tracing::error!("{err}");
+                yield Ok(Event::from(err));
+            }
+        } else {
+            let err = InferError::from(ValidationError::BestOfStream);
+            metrics::increment_counter!("tgi_request_failure", "err" => "validation");
+            tracing::error!("{err}");
+            yield Ok(Event::from(err));
+        }
+    };
+
+    (headers, Sse::new(stream).keep_alive(KeepAlive::default()))
+}
+
+/// Prometheus metrics scrape endpoint
+#[utoipa::path(
+    get,
+    tag = "Text Generation Inference",
+    path = "/metrics",
+    responses((status = 200, description = "Prometheus Metrics", body = String))
+)]
+async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String {
+    prom_handle.render()
+}
+
+/// Serving method
+#[allow(clippy::too_many_arguments)]
+pub async fn run(
+    model_info: HubModelInfo,
+    shard_info: ShardInfo,
+    compat_return_full_text: bool,
+    max_concurrent_requests: usize,
+    max_best_of: usize,
+    max_stop_sequences: usize,
+    max_input_length: usize,
+    max_total_tokens: usize,
+    waiting_served_ratio: f32,
+    max_batch_total_tokens: u32,
+    max_waiting_tokens: usize,
+    client: ShardedClient,
+    tokenizer: Option<Tokenizer>,
+    validation_workers: usize,
+    addr: SocketAddr,
+    allow_origin: Option<AllowOrigin>,
+) {
+    // OpenAPI documentation
+    #[derive(OpenApi)]
+    #[openapi(
+        paths(
+            get_model_info,
+            compat_generate,
+            generate,
+            generate_stream,
+            metrics,
+        ),
+        components(
+            schemas(
+                Info,
+                CompatGenerateRequest,
+                GenerateRequest,
+                GenerateParameters,
+                PrefillToken,
+                Token,
+                GenerateResponse,
+                BestOfSequence,
+                Details,
+                FinishReason,
+                StreamResponse,
+                StreamDetails,
+                ErrorResponse,
+            )
+        ),
+        tags(
+            (name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
+        ),
+        info(
+            title = "Text Generation Inference",
+            license(
+                name = "Apache 2.0",
+                url = "https://www.apache.org/licenses/LICENSE-2.0"
+            )
+        )
+    )]
+    struct ApiDoc;
+
+    // Create state
+    let validation = Validation::new(
+        validation_workers,
+        tokenizer,
+        max_best_of,
+        max_stop_sequences,
+        max_input_length,
+        max_total_tokens,
+    );
+    let generation_health = Arc::new(AtomicBool::new(false));
+    let health_ext = Health::new(client.clone(), generation_health.clone());
+    let infer = Infer::new(
+        client,
+        validation,
+        waiting_served_ratio,
+        max_batch_total_tokens,
+        max_waiting_tokens,
+        max_concurrent_requests,
+        shard_info.requires_padding,
+        generation_health,
+    );
+
+    // Duration buckets
+    let duration_matcher = Matcher::Suffix(String::from("duration"));
+    let n_duration_buckets = 35;
+    let mut duration_buckets = Vec::with_capacity(n_duration_buckets);
+    // Minimum duration in seconds
+    let mut value = 0.0001;
+    for _ in 0..n_duration_buckets {
+        // geometric sequence
+        value *= 1.5;
+        duration_buckets.push(value);
+    }
+    // Input Length buckets
+    let input_length_matcher = Matcher::Full(String::from("tgi_request_input_length"));
+    let input_length_buckets: Vec<f64> = (0..100)
+        .map(|x| (max_input_length as f64 / 100.0) * (x + 1) as f64)
+        .collect();
+    // Generated tokens buckets
+    let generated_tokens_matcher = Matcher::Full(String::from("tgi_request_generated_tokens"));
+    let generated_tokens_buckets: Vec<f64> = (0..100)
+        .map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
+        .collect();
+    // Input Length buckets
+    let max_new_tokens_matcher = Matcher::Full(String::from("tgi_request_max_new_tokens"));
+    let max_new_tokens_buckets: Vec<f64> = (0..100)
+        .map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
+        .collect();
+    // Batch size buckets
+    let batch_size_matcher = Matcher::Full(String::from("tgi_batch_next_size"));
+    let batch_size_buckets: Vec<f64> = (0..1024).map(|x| (x + 1) as f64).collect();
+
+    // Prometheus handler
+    let builder = PrometheusBuilder::new()
+        .set_buckets_for_metric(duration_matcher, &duration_buckets)
+        .unwrap()
+        .set_buckets_for_metric(input_length_matcher, &input_length_buckets)
+        .unwrap()
+        .set_buckets_for_metric(generated_tokens_matcher, &generated_tokens_buckets)
+        .unwrap()
+        .set_buckets_for_metric(max_new_tokens_matcher, &max_new_tokens_buckets)
+        .unwrap()
+        .set_buckets_for_metric(batch_size_matcher, &batch_size_buckets)
+        .unwrap();
+    let prom_handle = builder
+        .install_recorder()
+        .expect("failed to install metrics recorder");
+
+    // CORS layer
+    let allow_origin = allow_origin.unwrap_or(AllowOrigin::any());
+    let cors_layer = CorsLayer::new()
+        .allow_methods([Method::GET, Method::POST])
+        .allow_headers([http::header::CONTENT_TYPE])
+        .allow_origin(allow_origin);
+
+    // Endpoint info
+    let info = Info {
+        model_id: model_info.model_id,
+        model_sha: model_info.sha,
+        model_dtype: shard_info.dtype,
+        model_device_type: shard_info.device_type,
+        model_pipeline_tag: model_info.pipeline_tag,
+        max_concurrent_requests,
+        max_best_of,
+        max_stop_sequences,
+        max_input_length,
+        max_total_tokens,
+        waiting_served_ratio,
+        max_batch_total_tokens,
+        max_waiting_tokens,
+        validation_workers,
+        version: env!("CARGO_PKG_VERSION"),
+        sha: option_env!("VERGEN_GIT_SHA"),
+        docker_label: option_env!("DOCKER_LABEL"),
+    };
+
+    // Create router
+    let app = Router::new()
+        .merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", ApiDoc::openapi()))
+        // Base routes
+        .route("/", post(compat_generate))
+        .route("/info", get(get_model_info))
+        .route("/generate", post(generate))
+        .route("/generate_stream", post(generate_stream))
+        // AWS Sagemaker route
+        .route("/invocations", post(compat_generate))
+        // Base Health route
+        .route("/health", get(health))
+        // Inference API health route
+        .route("/", get(health))
+        // AWS Sagemaker health route
+        .route("/ping", get(health))
+        // Prometheus metrics route
+        .route("/metrics", get(metrics))
+        .layer(Extension(info))
+        .layer(Extension(health_ext))
+        .layer(Extension(compat_return_full_text))
+        .layer(Extension(infer))
+        .layer(Extension(prom_handle))
+        .layer(opentelemetry_tracing_layer())
+        .layer(cors_layer);
+
+    // Run server
+    axum::Server::bind(&addr)
+        .serve(app.into_make_service())
+        // Wait until all requests are finished to shut down
+        .with_graceful_shutdown(shutdown_signal())
+        .await
+        .unwrap();
+}
+
+/// Shutdown signal handler
+async fn shutdown_signal() {
+    let ctrl_c = async {
+        signal::ctrl_c()
+            .await
+            .expect("failed to install Ctrl+C handler");
+    };
+
+    #[cfg(unix)]
+    let terminate = async {
+        signal::unix::signal(signal::unix::SignalKind::terminate())
+            .expect("failed to install signal handler")
+            .recv()
+            .await;
+    };
+
+    #[cfg(not(unix))]
+    let terminate = std::future::pending::<()>();
+
+    tokio::select! {
+        _ = ctrl_c => {},
+        _ = terminate => {},
+    }
+
+    tracing::info!("signal received, starting graceful shutdown");
+    opentelemetry::global::shutdown_tracer_provider();
+}
+
+impl From<i32> for FinishReason {
+    fn from(finish_reason: i32) -> Self {
+        let finish_reason = text_generation_client::FinishReason::from_i32(finish_reason).unwrap();
+        match finish_reason {
+            text_generation_client::FinishReason::Length => FinishReason::Length,
+            text_generation_client::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
+            text_generation_client::FinishReason::StopSequence => FinishReason::StopSequence,
+        }
+    }
+}
+
+/// Convert to Axum supported formats
+impl From<InferError> for (StatusCode, Json<ErrorResponse>) {
+    fn from(err: InferError) -> Self {
+        let status_code = match err {
+            InferError::GenerationError(_) => StatusCode::FAILED_DEPENDENCY,
+            InferError::Overloaded(_) => StatusCode::TOO_MANY_REQUESTS,
+            InferError::ValidationError(_) => StatusCode::UNPROCESSABLE_ENTITY,
+            InferError::IncompleteGeneration => StatusCode::INTERNAL_SERVER_ERROR,
+        };
+
+        (
+            status_code,
+            Json(ErrorResponse {
+                error: err.to_string(),
+                error_type: err.error_type().to_string(),
+            }),
+        )
+    }
+}
+
+impl From<InferError> for Event {
+    fn from(err: InferError) -> Self {
+        Event::default()
+            .json_data(ErrorResponse {
+                error: err.to_string(),
+                error_type: err.error_type().to_string(),
+            })
+            .unwrap()
+    }
+}
diff --git a/router/src/validation.rs b/router/src/validation.rs
new file mode 100644
index 0000000000000000000000000000000000000000..cbb0d9cd1d662cb99828406cabcf125c58734198
--- /dev/null
+++ b/router/src/validation.rs
@@ -0,0 +1,533 @@
+use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
+/// Payload validation logic
+use crate::{GenerateParameters, GenerateRequest};
+use rand::{thread_rng, Rng};
+use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
+use thiserror::Error;
+use tokenizers::tokenizer::Tokenizer;
+use tokenizers::TruncationDirection;
+use tokio::sync::oneshot;
+use tracing::{instrument, Span};
+
+/// Validation
+#[derive(Debug, Clone)]
+pub struct Validation {
+    /// Validation parameters
+    max_best_of: usize,
+    max_stop_sequences: usize,
+    max_input_length: usize,
+    max_total_tokens: usize,
+    /// Channel to communicate with the background tokenization task
+    sender: Option<flume::Sender<TokenizerRequest>>,
+}
+
+impl Validation {
+    pub(crate) fn new(
+        workers: usize,
+        tokenizer: Option<Tokenizer>,
+        max_best_of: usize,
+        max_stop_sequences: usize,
+        max_input_length: usize,
+        max_total_tokens: usize,
+    ) -> Self {
+        if max_input_length >= max_total_tokens {
+            panic!("`max_input_length` must be < `max_total_tokens`");
+        }
+
+        // If we have a fast tokenizer
+        let sender = if let Some(tokenizer) = tokenizer {
+            // Create channel
+            let (validation_sender, validation_receiver) = flume::unbounded();
+
+            // Create workers
+            for _ in 0..workers {
+                let tokenizer_clone = tokenizer.clone();
+                let receiver_clone = validation_receiver.clone();
+
+                // Spawn worker
+                tokio::task::spawn_blocking(move || {
+                    tokenizer_worker(tokenizer_clone, receiver_clone)
+                });
+            }
+            Some(validation_sender)
+        } else {
+            None
+        };
+
+        Self {
+            max_best_of,
+            sender,
+            max_stop_sequences,
+            max_input_length,
+            max_total_tokens,
+        }
+    }
+
+    #[instrument(skip_all)]
+    async fn validate_input(
+        &self,
+        inputs: String,
+        truncate: Option<usize>,
+        max_new_tokens: u32,
+    ) -> Result<(String, usize), ValidationError> {
+        // If we have a fast tokenizer
+        if let Some(sender) = &self.sender {
+            // Create response channel
+            let (response_sender, response_receiver) = oneshot::channel();
+            // Send request to the background validation task
+            // Unwrap is safe here
+            sender
+                .send(((inputs, truncate), response_sender, Span::current()))
+                .unwrap();
+
+            // Await on response channel
+            // Unwrap is safe here
+            let (inputs, input_length) = response_receiver.await.unwrap()?;
+
+            // Get total tokens
+            let total_tokens = input_length + max_new_tokens as usize;
+
+            // Validate MaxTotalTokens
+            if total_tokens > self.max_total_tokens {
+                return Err(ValidationError::MaxTotalTokens(
+                    self.max_total_tokens,
+                    input_length,
+                    max_new_tokens,
+                ));
+            }
+
+            // Validate InputLength
+            if input_length > self.max_input_length {
+                return Err(ValidationError::InputLength(
+                    self.max_input_length,
+                    input_length,
+                ));
+            }
+
+            metrics::histogram!("tgi_request_input_length", input_length as f64);
+            Ok((inputs, input_length))
+        }
+        // Return inputs without validation
+        else {
+            // In this case, we don't know the real length in tokens of the inputs
+            // However, the inputs will be truncated by the python servers
+            // We make sure that truncate + max_new_tokens <= self.max_total_tokens
+            let input_length = truncate.unwrap_or(self.max_input_length);
+
+            // Validate MaxNewTokens
+            if (input_length as u32 + max_new_tokens) > self.max_total_tokens as u32 {
+                return Err(ValidationError::MaxNewTokens(
+                    self.max_total_tokens - self.max_input_length,
+                    max_new_tokens,
+                ));
+            }
+
+            Ok((inputs, input_length))
+        }
+    }
+
+    /// Validate a payload and get the number of tokens in the input
+    #[instrument(skip_all)]
+    pub(crate) async fn validate(
+        &self,
+        request: GenerateRequest,
+    ) -> Result<ValidGenerateRequest, ValidationError> {
+        let GenerateParameters {
+            best_of,
+            temperature,
+            repetition_penalty,
+            top_k,
+            top_p,
+            typical_p,
+            do_sample,
+            max_new_tokens,
+            stop: stop_sequences,
+            truncate,
+            seed,
+            watermark,
+            ..
+        } = request.parameters;
+
+        // sampling must be true when best_of > 1
+        let best_of = best_of.unwrap_or(1);
+        let sampling = do_sample
+            || temperature.is_some()
+            || top_k.is_some()
+            || top_p.is_some()
+            || typical_p.is_some();
+
+        if best_of > 1 && !sampling {
+            return Err(BestOfSampling);
+        }
+
+        let temperature = temperature.unwrap_or(1.0);
+        if temperature <= 0.0 {
+            return Err(ValidationError::Temperature);
+        }
+
+        let repetition_penalty = repetition_penalty.unwrap_or(1.0);
+        if repetition_penalty <= 0.0 {
+            return Err(ValidationError::RepetitionPenalty);
+        }
+
+        // Different because the proto default value is not a valid value
+        // for the user
+        let top_p = top_p
+            .map(|value| {
+                if value <= 0.0 || value >= 1.0 {
+                    return Err(ValidationError::TopP);
+                }
+                Ok(value)
+            })
+            .unwrap_or(Ok(1.0))?;
+
+        let typical_p = typical_p
+            .map(|value| {
+                if value <= 0.0 || value >= 1.0 {
+                    return Err(ValidationError::TypicalP);
+                }
+                Ok(value)
+            })
+            .unwrap_or(Ok(1.0))?;
+
+        let top_k: u32 = top_k
+            .map(|value| {
+                if value <= 0 {
+                    return Err(ValidationError::TopK);
+                }
+                Ok(value as u32)
+            })
+            .unwrap_or(Ok(0))?;
+
+        if max_new_tokens == 0 {
+            return Err(ValidationError::NegativeMaxNewTokens);
+        }
+
+        if stop_sequences.len() > self.max_stop_sequences {
+            return Err(ValidationError::StopSequence(
+                self.max_stop_sequences,
+                stop_sequences.len(),
+            ));
+        }
+
+        // If seed is None, assign a random one
+        let seed = match seed {
+            None => thread_rng().gen(),
+            Some(seed) => {
+                if best_of > 1 {
+                    return Err(BestOfSeed);
+                }
+                seed
+            }
+        };
+
+        // Check if inputs is empty
+        if request.inputs.is_empty() {
+            return Err(EmptyInput);
+        }
+
+        // Check if truncate is strictly positive and less than max_input_length
+        let truncate = truncate
+            .map(|value| {
+                if value == 0 || value > self.max_input_length {
+                    return Err(ValidationError::Truncate(self.max_input_length, value));
+                }
+                Ok(Some(value))
+            })
+            .unwrap_or(Ok(None))?;
+
+        // Validate inputs
+        let (inputs, input_length) = self
+            .validate_input(request.inputs, truncate, max_new_tokens)
+            .await?;
+
+        let parameters = NextTokenChooserParameters {
+            temperature,
+            repetition_penalty,
+            top_k,
+            top_p,
+            typical_p,
+            do_sample,
+            seed,
+            watermark,
+        };
+        let stopping_parameters = StoppingCriteriaParameters {
+            max_new_tokens,
+            stop_sequences,
+            ignore_eos_token: false,
+        };
+
+        metrics::histogram!("tgi_request_max_new_tokens", max_new_tokens as f64);
+
+        Ok(ValidGenerateRequest {
+            inputs,
+            input_length: input_length as u32,
+            truncate: truncate.unwrap_or(self.max_input_length) as u32,
+            parameters,
+            stopping_parameters,
+        })
+    }
+
+    /// Validate the best_of parameter
+    #[instrument(skip_all)]
+    pub(crate) fn validate_best_of(&self, best_of: usize) -> Result<usize, ValidationError> {
+        if self.max_best_of == 1 && best_of != 1 {
+            return Err(ValidationError::BestOfDisabled);
+        }
+
+        if best_of > self.max_best_of {
+            return Err(ValidationError::BestOf(self.max_best_of, best_of));
+        }
+
+        Ok(best_of)
+    }
+}
+
+/// Start tokenization workers
+fn tokenizer_worker(tokenizer: Tokenizer, receiver: flume::Receiver<TokenizerRequest>) {
+    // Loop over requests
+    while let Ok(((inputs, truncate), response_tx, parent_span)) = receiver.recv() {
+        parent_span.in_scope(|| {
+            response_tx
+                .send(prepare_input(inputs, truncate, &tokenizer))
+                .unwrap_or(())
+        })
+    }
+}
+
+/// Get input length and optionally truncate it
+fn prepare_input(
+    inputs: String,
+    truncate: Option<usize>,
+    tokenizer: &Tokenizer,
+) -> Result<(String, usize), ValidationError> {
+    // Get the number of tokens in the input
+    let mut encoding = tokenizer
+        .encode(inputs.clone(), true)
+        .map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
+
+    // Optionally truncate
+    let (inputs, input_length) = match truncate {
+        // Truncate is some and < encoding length
+        Some(truncate) if truncate < encoding.len() => {
+            // truncate encoding and decode new inputs
+            encoding.truncate(truncate, 0, TruncationDirection::Left);
+            let inputs = tokenizer
+                .decode(Vec::from(encoding.get_ids()), false)
+                .map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
+            (inputs, encoding.len())
+        }
+        // Nothing to do
+        _ => (inputs, encoding.len()),
+    };
+
+    Ok((inputs, input_length))
+}
+
+type TokenizerRequest = (
+    (String, Option<usize>),
+    oneshot::Sender<Result<(String, usize), ValidationError>>,
+    Span,
+);
+
+#[derive(Debug)]
+pub(crate) struct ValidGenerateRequest {
+    pub inputs: String,
+    pub input_length: u32,
+    pub truncate: u32,
+    pub parameters: NextTokenChooserParameters,
+    pub stopping_parameters: StoppingCriteriaParameters,
+}
+
+#[derive(Error, Debug)]
+pub enum ValidationError {
+    #[error("`best_of` must be > 0 and <= {0}. Given: {1}")]
+    BestOf(usize, usize),
+    #[error("`best_of` != 1 is not allowed for this endpoint")]
+    BestOfDisabled,
+    #[error("you must use sampling when `best_of` is > 1")]
+    BestOfSampling,
+    #[error("`seed` must not be set when `best_of` > 1")]
+    BestOfSeed,
+    #[error("`best_of` != 1 is not supported when streaming tokens")]
+    BestOfStream,
+    #[error("`temperature` must be strictly positive")]
+    Temperature,
+    #[error("`repetition_penalty` must be strictly positive")]
+    RepetitionPenalty,
+    #[error("`top_p` must be > 0.0 and < 1.0")]
+    TopP,
+    #[error("`top_k` must be strictly positive")]
+    TopK,
+    #[error("`truncate` must be strictly positive and less than {0}. Given: {1}")]
+    Truncate(usize, usize),
+    #[error("`typical_p` must be > 0.0 and < 1.0")]
+    TypicalP,
+    #[error("`max_new_tokens` must be strictly positive")]
+    NegativeMaxNewTokens,
+    #[error("`max_new_tokens` must be <= {0}. Given: {1}")]
+    MaxNewTokens(usize, u32),
+    #[error("`inputs` tokens + `max_new_tokens` must be <= {0}. Given: {1} `inputs` tokens and {2} `max_new_tokens`")]
+    MaxTotalTokens(usize, usize, u32),
+    #[error("`inputs` must have less than {0} tokens. Given: {1}")]
+    InputLength(usize, usize),
+    #[error("`inputs` cannot be empty")]
+    EmptyInput,
+    #[error("`stop` supports up to {0} stop sequences. Given: {1}")]
+    StopSequence(usize, usize),
+    #[error("tokenizer error {0}")]
+    Tokenizer(String),
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::default_parameters;
+    use crate::tests::get_tokenizer;
+
+    #[tokio::test]
+    async fn test_validation_max_new_tokens() {
+        let tokenizer = None;
+        let max_best_of = 2;
+        let max_stop_sequence = 3;
+        let max_input_length = 4;
+        let max_total_tokens = 5;
+        let workers = 1;
+        let validation = Validation::new(
+            workers,
+            tokenizer,
+            max_best_of,
+            max_stop_sequence,
+            max_input_length,
+            max_total_tokens,
+        );
+
+        let max_new_tokens = 10;
+        match validation
+            .validate_input("Hello".to_string(), None, max_new_tokens)
+            .await
+        {
+            Err(ValidationError::MaxNewTokens(1, 10)) => (),
+            _ => panic!("Unexpected not max new tokens"),
+        }
+    }
+
+    #[tokio::test]
+    async fn test_validation_input_length() {
+        let tokenizer = Some(get_tokenizer().await);
+        let max_best_of = 2;
+        let max_stop_sequence = 3;
+        let max_input_length = 4;
+        let max_total_tokens = 5;
+        let workers = 1;
+        let validation = Validation::new(
+            workers,
+            tokenizer,
+            max_best_of,
+            max_stop_sequence,
+            max_input_length,
+            max_total_tokens,
+        );
+
+        let max_new_tokens = 10;
+        match validation
+            .validate_input("Hello".to_string(), None, max_new_tokens)
+            .await
+        {
+            Err(ValidationError::MaxTotalTokens(5, 1, 10)) => (),
+            _ => panic!("Unexpected not max new tokens"),
+        }
+    }
+
+    #[tokio::test]
+    async fn test_validation_best_of_sampling() {
+        let tokenizer = Some(get_tokenizer().await);
+        let max_best_of = 2;
+        let max_stop_sequence = 3;
+        let max_input_length = 4;
+        let max_total_tokens = 5;
+        let workers = 1;
+        let validation = Validation::new(
+            workers,
+            tokenizer,
+            max_best_of,
+            max_stop_sequence,
+            max_input_length,
+            max_total_tokens,
+        );
+        match validation
+            .validate(GenerateRequest {
+                inputs: "Hello".to_string(),
+                parameters: GenerateParameters {
+                    best_of: Some(2),
+                    do_sample: false,
+                    ..default_parameters()
+                },
+            })
+            .await
+        {
+            Err(ValidationError::BestOfSampling) => (),
+            _ => panic!("Unexpected not best of sampling"),
+        }
+    }
+
+    #[tokio::test]
+    async fn test_validation_top_p() {
+        let tokenizer = Some(get_tokenizer().await);
+        let max_best_of = 2;
+        let max_stop_sequence = 3;
+        let max_input_length = 4;
+        let max_total_tokens = 5;
+        let workers = 1;
+        let validation = Validation::new(
+            workers,
+            tokenizer,
+            max_best_of,
+            max_stop_sequence,
+            max_input_length,
+            max_total_tokens,
+        );
+        match validation
+            .validate(GenerateRequest {
+                inputs: "Hello".to_string(),
+                parameters: GenerateParameters {
+                    top_p: Some(1.0),
+                    ..default_parameters()
+                },
+            })
+            .await
+        {
+            Err(ValidationError::TopP) => (),
+            _ => panic!("Unexpected top_p"),
+        }
+
+        match validation
+            .validate(GenerateRequest {
+                inputs: "Hello".to_string(),
+                parameters: GenerateParameters {
+                    top_p: Some(0.99),
+                    max_new_tokens: 1,
+                    ..default_parameters()
+                },
+            })
+            .await
+        {
+            Ok(_) => (),
+            _ => panic!("Unexpected top_p error"),
+        }
+
+        let valid_request = validation
+            .validate(GenerateRequest {
+                inputs: "Hello".to_string(),
+                parameters: GenerateParameters {
+                    top_p: None,
+                    max_new_tokens: 1,
+                    ..default_parameters()
+                },
+            })
+            .await
+            .unwrap();
+        // top_p == 1.0 is invalid for users to ask for but it's the default resolved value.
+        assert_eq!(valid_request.parameters.top_p, 1.0);
+    }
+}
diff --git a/rust-toolchain.toml b/rust-toolchain.toml
new file mode 100644
index 0000000000000000000000000000000000000000..d6ecf0c44b6f0bdc63681eef5e2dd8f1781ed140
--- /dev/null
+++ b/rust-toolchain.toml
@@ -0,0 +1,3 @@
+[toolchain]
+channel = "1.69.0"
+components = ["rustfmt", "clippy"]
\ No newline at end of file
diff --git a/sagemaker-entrypoint.sh b/sagemaker-entrypoint.sh
new file mode 100644
index 0000000000000000000000000000000000000000..711e3721b300b4279306bdc1a9377e5c420ec498
--- /dev/null
+++ b/sagemaker-entrypoint.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+if [[ -z "${HF_MODEL_ID}" ]]; then
+  echo "HF_MODEL_ID must be set"
+  exit 1
+fi
+
+if [[ -n "${HF_MODEL_REVISION}" ]]; then
+  export REVISION="${HF_MODEL_REVISION}"
+fi
+
+if [[ -n "${SM_NUM_GPUS}" ]]; then
+  export NUM_SHARD="${SM_NUM_GPUS}"
+fi
+
+if [[ -n "${HF_MODEL_QUANTIZE}" ]]; then
+  export QUANTIZE="${HF_MODEL_QUANTIZE}"
+fi
+
+text-generation-launcher --port 8080
\ No newline at end of file
diff --git a/server/Makefile b/server/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..150d7e4a31883754ac4de4fbaa07773c80267b74
--- /dev/null
+++ b/server/Makefile
@@ -0,0 +1,29 @@
+include Makefile-transformers
+include Makefile-flash-att
+
+unit-tests:
+	python -m pytest tests
+
+gen-server:
+	# Compile protos
+	pip install grpcio-tools==1.51.1 mypy-protobuf==3.4.0 'types-protobuf>=3.20.4' --no-cache-dir
+	mkdir text_generation_server/pb || true
+	python -m grpc_tools.protoc -I../proto --python_out=text_generation_server/pb \
+		--grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/generate.proto
+	find text_generation_server/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \;
+	touch text_generation_server/pb/__init__.py
+
+install-torch:
+	# Install specific version of torch
+	pip install torch --extra-index-url https://download.pytorch.org/whl/cu118 --no-cache-dir
+
+install: gen-server install-torch install-transformers
+	pip install pip --upgrade
+	pip install -r requirements.txt
+	pip install -e ".[bnb, accelerate]"
+
+run-dev:
+	SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
+
+export-requirements:
+	poetry export -o requirements.txt -E bnb --without-hashes
\ No newline at end of file
diff --git a/server/Makefile-flash-att b/server/Makefile-flash-att
new file mode 100644
index 0000000000000000000000000000000000000000..ad894bfa6936f4c42278bacb4091d9e7f947a234
--- /dev/null
+++ b/server/Makefile-flash-att
@@ -0,0 +1,16 @@
+flash_att_commit := d478eeec8f16c7939c54e4617dbd36f59b8eeed7
+
+flash-attention:
+    # Clone flash attention
+	pip install packaging
+	git clone https://github.com/HazyResearch/flash-attention.git
+
+build-flash-attention: flash-attention
+	cd flash-attention && git fetch && git checkout $(flash_att_commit)
+	cd flash-attention && python setup.py build
+	cd flash-attention/csrc/rotary && python setup.py build
+	cd flash-attention/csrc/layer_norm && python setup.py build
+
+install-flash-attention: build-flash-attention
+	pip uninstall flash_attn rotary_emb dropout_layer_norm -y || true
+	cd flash-attention && python setup.py install && cd csrc/layer_norm && python setup.py install && cd ../rotary && python setup.py install
\ No newline at end of file
diff --git a/server/Makefile-transformers b/server/Makefile-transformers
new file mode 100644
index 0000000000000000000000000000000000000000..64d0167222df5a2c33a70cfe0dd38d1cee888b68
--- /dev/null
+++ b/server/Makefile-transformers
@@ -0,0 +1,13 @@
+transformers_commit := 69009822aa7897ffab97afb814e38126b83f639e
+
+transformers:
+	# Clone fork of transformers with custom CUDA kernels and sharding logic
+	pip install --upgrade setuptools
+	git clone https://github.com/OlivierDehaene/transformers.git
+
+build-transformers: transformers
+	cd transformers && git fetch && git checkout $(transformers_commit) && python setup.py build
+
+install-transformers: build-transformers
+	pip uninstall transformers -y || true
+	cd transformers && python setup.py install
\ No newline at end of file
diff --git a/server/poetry.lock b/server/poetry.lock
new file mode 100644
index 0000000000000000000000000000000000000000..b5c03a57eec1f8f2e32b06438274df3fdd96e867
--- /dev/null
+++ b/server/poetry.lock
@@ -0,0 +1,1757 @@
+[[package]]
+name = "accelerate"
+version = "0.15.0"
+description = "Accelerate"
+category = "main"
+optional = true
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+torch = ">=1.4.0"
+
+[package.extras]
+dev = ["black (>=22.0,<23.0)", "datasets", "deepspeed (<0.7.0)", "evaluate", "flake8 (>=3.8.3)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "rich", "scikit-learn", "scipy", "tqdm", "transformers"]
+quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)"]
+rich = ["rich"]
+sagemaker = ["sagemaker"]
+test_dev = ["datasets", "deepspeed (<0.7.0)", "evaluate", "scikit-learn", "scipy", "tqdm", "transformers"]
+test_prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"]
+test_trackers = ["comet-ml", "tensorboard", "wandb"]
+testing = ["datasets", "deepspeed (<0.7.0)", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "tqdm", "transformers"]
+
+[[package]]
+name = "backoff"
+version = "2.2.1"
+description = "Function decoration for backoff and retry"
+category = "main"
+optional = false
+python-versions = ">=3.7,<4.0"
+
+[[package]]
+name = "bitsandbytes"
+version = "0.38.1"
+description = "8-bit optimizers and matrix multiplication routines."
+category = "main"
+optional = true
+python-versions = "*"
+
+[[package]]
+name = "certifi"
+version = "2022.12.7"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "charset-normalizer"
+version = "3.1.0"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[[package]]
+name = "click"
+version = "8.1.3"
+description = "Composable command line interface toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "cmake"
+version = "3.26.3"
+description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
+category = "main"
+optional = true
+python-versions = "*"
+
+[package.extras]
+test = ["codecov (>=2.0.5)", "coverage (>=4.2)", "flake8 (>=3.0.4)", "path.py (>=11.5.0)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)", "pytest-runner (>=2.9)", "pytest-virtualenv (>=1.7.0)", "scikit-build (>=0.10.0)", "setuptools (>=28.0.0)", "virtualenv (>=15.0.3)", "wheel"]
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diff --git a/server/pyproject.toml b/server/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..22aa41db9b4e806ca86802167821e5de40eea1d0
--- /dev/null
+++ b/server/pyproject.toml
@@ -0,0 +1,40 @@
+[tool.poetry]
+name = "text-generation-server"
+version = "0.6.0"
+description = "Text Generation Inference Python gRPC Server"
+authors = ["Olivier Dehaene <olivier@huggingface.co>"]
+
+[tool.poetry.scripts]
+text-generation-server = 'text_generation_server.cli:app'
+
+[tool.poetry.dependencies]
+python = "^3.9"
+protobuf = "^4.21.7"
+grpcio = "^1.51.1"
+grpcio-status = "^1.51.1"
+grpcio-reflection = "^1.51.1"
+grpc-interceptor = "^0.15.0"
+typer = "^0.6.1"
+accelerate = { version = "^0.15.0", optional = true }
+bitsandbytes = { version = "^0.38.1", optional = true }
+safetensors = "0.3.1"
+loguru = "^0.6.0"
+opentelemetry-api = "^1.15.0"
+opentelemetry-exporter-otlp = "^1.15.0"
+opentelemetry-instrumentation-grpc = "^0.36b0"
+hf-transfer = "^0.1.2"
+sentencepiece = "^0.1.97"
+tokenizers = "0.13.3"
+huggingface-hub = "0.14.0"
+
+[tool.poetry.extras]
+accelerate = ["accelerate"]
+bnb = ["bitsandbytes"]
+
+[tool.poetry.group.dev.dependencies]
+grpcio-tools = "^1.51.1"
+pytest = "^7.3.0"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
diff --git a/server/requirements.txt b/server/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ad48bff944ff70b8491aa61e2397a90fd7957fbd
--- /dev/null
+++ b/server/requirements.txt
@@ -0,0 +1,41 @@
+backoff==2.2.1 ; python_version >= "3.9" and python_version < "4.0"
+bitsandbytes==0.38.1 ; python_version >= "3.9" and python_version < "4.0"
+certifi==2022.12.7 ; python_version >= "3.9" and python_version < "4"
+charset-normalizer==3.1.0 ; python_version >= "3.9" and python_version < "4"
+click==8.1.3 ; python_version >= "3.9" and python_version < "4.0"
+colorama==0.4.6 ; python_version >= "3.9" and python_version < "4.0" and sys_platform == "win32" or python_version >= "3.9" and python_version < "4.0" and platform_system == "Windows"
+deprecated==1.2.13 ; python_version >= "3.9" and python_version < "4.0"
+filelock==3.12.0 ; python_version >= "3.9" and python_version < "4.0"
+fsspec==2023.4.0 ; python_version >= "3.9" and python_version < "4.0"
+googleapis-common-protos==1.59.0 ; python_version >= "3.9" and python_version < "4.0"
+grpc-interceptor==0.15.1 ; python_version >= "3.9" and python_version < "4.0"
+grpcio-reflection==1.54.0 ; python_version >= "3.9" and python_version < "4.0"
+grpcio-status==1.54.0 ; python_version >= "3.9" and python_version < "4.0"
+grpcio==1.54.0 ; python_version >= "3.9" and python_version < "4.0"
+hf-transfer==0.1.3 ; python_version >= "3.9" and python_version < "4.0"
+huggingface-hub==0.14.0 ; python_version >= "3.9" and python_version < "4.0"
+idna==3.4 ; python_version >= "3.9" and python_version < "4"
+loguru==0.6.0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-exporter-otlp==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-instrumentation-grpc==0.36b0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
+opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "4.0"
+packaging==23.1 ; python_version >= "3.9" and python_version < "4.0"
+protobuf==4.22.3 ; python_version >= "3.9" and python_version < "4.0"
+pyyaml==6.0 ; python_version >= "3.9" and python_version < "4.0"
+requests==2.28.2 ; python_version >= "3.9" and python_version < "4"
+safetensors==0.3.1 ; python_version >= "3.9" and python_version < "4.0"
+sentencepiece==0.1.98 ; python_version >= "3.9" and python_version < "4.0"
+setuptools==67.7.2 ; python_version >= "3.9" and python_version < "4.0"
+tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "4.0"
+tqdm==4.65.0 ; python_version >= "3.9" and python_version < "4.0"
+typer==0.6.1 ; python_version >= "3.9" and python_version < "4.0"
+typing-extensions==4.5.0 ; python_version >= "3.9" and python_version < "4.0"
+urllib3==1.26.15 ; python_version >= "3.9" and python_version < "4"
+win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "4.0" and sys_platform == "win32"
+wrapt==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
diff --git a/server/tests/conftest.py b/server/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..16d2c4081ffc70b4faefdff2c29b2c1df2683ae0
--- /dev/null
+++ b/server/tests/conftest.py
@@ -0,0 +1,20 @@
+import pytest
+
+from text_generation_server.pb import generate_pb2
+
+
+@pytest.fixture
+def default_pb_parameters():
+    return generate_pb2.NextTokenChooserParameters(
+        temperature=1.0,
+        repetition_penalty=1.0,
+        top_k=0,
+        top_p=1.0,
+        typical_p=1.0,
+        do_sample=False,
+    )
+
+
+@pytest.fixture
+def default_pb_stop_parameters():
+    return generate_pb2.StoppingCriteriaParameters(stop_sequences=[], max_new_tokens=10)
diff --git a/server/tests/models/test_bloom.py b/server/tests/models/test_bloom.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0adab97be927d9771453a93c6ec3ffd94bb1e08
--- /dev/null
+++ b/server/tests/models/test_bloom.py
@@ -0,0 +1,336 @@
+import pytest
+import torch
+
+from copy import copy
+from transformers import AutoTokenizer
+
+from text_generation_server.pb import generate_pb2
+from text_generation_server.models.causal_lm import CausalLMBatch
+from text_generation_server.models.bloom import BloomCausalLMBatch, BLOOM
+
+
+@pytest.fixture(scope="session")
+def default_bloom():
+    return BLOOM("bigscience/bloom-560m")
+
+
+@pytest.fixture(scope="session")
+def bloom_560m_tokenizer():
+    return AutoTokenizer.from_pretrained("bigscience/bloom-560m", padding_side="left")
+
+
+@pytest.fixture
+def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
+    return generate_pb2.Request(
+        id=0,
+        inputs="Test",
+        truncate=100,
+        parameters=default_pb_parameters,
+        stopping_parameters=default_pb_stop_parameters,
+    )
+
+
+@pytest.fixture
+def default_pb_batch(default_pb_request):
+    return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
+
+
+@pytest.fixture
+def default_bloom_batch(default_pb_batch, bloom_560m_tokenizer):
+    return BloomCausalLMBatch.from_pb(
+        default_pb_batch, bloom_560m_tokenizer, torch.device("cpu")
+    )
+
+
+@pytest.fixture
+def default_multi_requests_bloom_batch(default_pb_request, bloom_560m_tokenizer):
+    req_0 = copy(default_pb_request)
+    req_0.id = 1
+    req_1 = default_pb_request
+    req_1.id = 2
+    req_1.stopping_parameters.max_new_tokens = 5
+
+    batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
+    return BloomCausalLMBatch.from_pb(
+        batch_pb, bloom_560m_tokenizer, torch.device("cpu")
+    )
+
+
+def test_batch_from_pb(default_pb_batch, default_bloom_batch):
+    batch = default_bloom_batch
+
+    assert batch.batch_id == default_pb_batch.id
+    assert batch.requests == default_pb_batch.requests
+
+    assert len(batch.input_ids) == default_pb_batch.size
+    assert batch.input_ids[0][-1] == 10264
+    assert torch.all(batch.input_ids[0][:-1] == 3)
+
+    assert batch.attention_mask[0][0] == 1
+    assert torch.all(batch.attention_mask[0][1:] == 0)
+
+    assert batch.past_key_values is None
+
+    assert all(
+        [
+            torch.equal(input_ids, all_input_ids[:, 0])
+            for input_ids, all_input_ids in zip(batch.input_ids, batch.all_input_ids)
+        ]
+    )
+
+    assert batch.input_lengths == [1]
+
+    assert len(batch) == default_pb_batch.size
+    assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
+
+    assert batch.max_input_length == batch.input_lengths[0]
+
+
+def test_batch_concatenate_no_prefill(default_bloom_batch):
+    with pytest.raises(ValueError):
+        BloomCausalLMBatch.concatenate([default_bloom_batch, default_bloom_batch])
+
+
+def test_causal_lm_batch_type(default_bloom):
+    assert default_bloom.batch_type == BloomCausalLMBatch
+
+
+def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
+    sequence_length = len(default_bloom_batch.all_input_ids[0])
+    generations, next_batch = default_bloom.generate_token(default_bloom_batch)
+
+    assert len(generations) == len(default_bloom_batch)
+    assert isinstance(next_batch, CausalLMBatch)
+    assert not next_batch.keys_head_dim_last
+
+    assert len(next_batch.all_input_ids) == len(next_batch)
+    assert len(next_batch.all_input_ids[0]) == sequence_length + 1
+    assert len(next_batch.attention_mask[0]) == 11
+    assert torch.all(next_batch.all_input_ids[0][-2:] == 10264)
+    assert torch.all(next_batch.all_input_ids[0][:-2] == 3)
+
+    assert torch.all(next_batch.attention_mask[0][:2] == 1)
+    assert torch.all(next_batch.attention_mask[0][2:] == 0)
+
+    assert next_batch.input_ids.shape == (len(next_batch), 1)
+    assert next_batch.input_ids[0, 0] == 10264
+
+    assert next_batch.input_lengths == [2]
+    assert next_batch.max_input_length == next_batch.input_lengths[0]
+
+    assert next_batch.past_key_values is not None
+    assert all(
+        [p[0].shape == (16, 64, sequence_length) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[1].shape == (16, sequence_length, 64) for p in next_batch.past_key_values]
+    )
+    assert all([generation.generated_text is None for generation in generations])
+    assert all([len(generation.prefill_tokens) == 1 for generation in generations])
+    assert all([generation.token_id.item() == 10264 for generation in generations])
+    assert all([generation.token_text == "Test" for generation in generations])
+    assert generations[0].request_id == 0
+
+
+def test_causal_lm_generate_token_completion(default_bloom, default_bloom_batch):
+    next_batch = default_bloom_batch
+    for _ in range(default_bloom_batch.stopping_criterias[0].max_new_tokens - 1):
+        generations, next_batch = default_bloom.generate_token(next_batch)
+        assert len(generations) == len(default_bloom_batch)
+
+    generations, next_batch = default_bloom.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert (
+        generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
+    )
+    assert generations[0].request_id == default_bloom_batch.requests[0].id
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_bloom_batch.stopping_criterias[0].max_new_tokens
+    )
+
+
+def test_causal_lm_generate_token_completion_multi(
+    default_bloom, default_multi_requests_bloom_batch
+):
+    next_batch = default_multi_requests_bloom_batch
+
+    for i in range(
+        default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens - 1
+    ):
+        generations, next_batch = default_bloom.generate_token(next_batch)
+        assert len(generations) == len(default_multi_requests_bloom_batch)
+
+    generations, next_batch = default_bloom.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 2
+    assert generations[1].generated_text.text == "TestTestTestTestTest"
+    assert (
+        generations[1].request_id == default_multi_requests_bloom_batch.requests[1].id
+    )
+    assert (
+        generations[1].generated_text.generated_tokens
+        == default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
+    )
+    # Copy stopping_criterias before filtering
+    stopping_criterias = default_multi_requests_bloom_batch.stopping_criterias.copy()
+
+    next_batch = next_batch.filter([next_batch.requests[0]])
+
+    for _ in range(
+        stopping_criterias[0].max_new_tokens - stopping_criterias[1].max_new_tokens - 1
+    ):
+        generations, next_batch = default_bloom.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_bloom.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert (
+        generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
+    )
+    assert (
+        generations[0].request_id == default_multi_requests_bloom_batch.requests[0].id
+    )
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_multi_requests_bloom_batch.stopping_criterias[0].max_new_tokens
+    )
+
+
+def test_batch_concatenate(
+    default_bloom, default_bloom_batch, default_multi_requests_bloom_batch
+):
+    next_batch_0 = default_bloom_batch
+    _, next_batch_0 = default_bloom.generate_token(next_batch_0)
+    _, next_batch_0 = default_bloom.generate_token(next_batch_0)
+
+    next_batch_1 = default_multi_requests_bloom_batch
+    _, next_batch_1 = default_bloom.generate_token(next_batch_1)
+
+    # Clone past_key_values before concatenating to compare after,
+    # because they are removed from the concatenated batches
+    next_batch_0_past_key_values = [
+        (k.clone(), v.clone()) for (k, v) in next_batch_0.past_key_values
+    ]
+    next_batch_1_past_key_values = [
+        (k.clone(), v.clone()) for (k, v) in next_batch_1.past_key_values
+    ]
+
+    next_batch = BloomCausalLMBatch.concatenate([next_batch_0, next_batch_1])
+
+    assert torch.equal(next_batch.all_input_ids[0], next_batch_0.all_input_ids[0])
+    assert torch.equal(next_batch.all_input_ids[1], next_batch_1.all_input_ids[0])
+    assert torch.equal(next_batch.all_input_ids[2], next_batch_1.all_input_ids[1])
+
+    assert torch.all(
+        next_batch.attention_mask[0, : -next_batch.padding_right_offset] == 1
+    )
+    assert torch.all(
+        next_batch.attention_mask[1:, 1 : -next_batch.padding_right_offset] == 1
+    )
+    assert torch.all(next_batch.attention_mask[1:, 3:] == 0)
+
+    assert next_batch.batch_id == 0
+    assert torch.all(next_batch.input_ids == 10264)
+
+    assert next_batch.input_lengths == [3, 2, 2]
+    assert next_batch.max_input_length == 3
+
+    assert next_batch.requests[0] == next_batch_0.requests[0]
+    assert next_batch.requests[1:] == next_batch_1.requests
+
+    assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0]
+    assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers
+
+    assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0]
+    assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias
+
+    assert next_batch.past_key_values is not None
+    assert all([p[0].shape == (3, 16, 64, 2) for p in next_batch.past_key_values])
+    assert all([p[1].shape == (3, 16, 2, 64) for p in next_batch.past_key_values])
+
+    for i, past in enumerate(next_batch.past_key_values):
+        assert torch.equal(next_batch_0_past_key_values[i][0][:, :, -2:], past[0][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][0][:, :, -1:],
+            past[0][1:, :, :, -1].reshape(-1, 64, 1),
+        )
+
+        assert torch.equal(next_batch_0_past_key_values[i][1][:, -2:, :], past[1][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][1][:, -1:, :],
+            past[1][1:, :, -1, :].reshape(-1, 1, 64),
+        )
+
+    for _ in range(
+        default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens - 2
+    ):
+        generations, next_batch = default_bloom.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_bloom.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 3
+    assert generations[2].generated_text.text == "TestTestTestTestTest"
+    assert (
+        generations[2].request_id == default_multi_requests_bloom_batch.requests[1].id
+    )
+    assert (
+        generations[2].generated_text.generated_tokens
+        == default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
+    )
+
+    next_batch = next_batch.filter([next_batch.requests[0], next_batch.requests[1]])
+
+    for _ in range(
+        default_bloom_batch.stopping_criterias[0].max_new_tokens
+        - default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
+        - 2
+    ):
+        generations, next_batch = default_bloom.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_bloom.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 2
+    assert (
+        generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
+    )
+    assert generations[0].request_id == default_bloom_batch.requests[0].id
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_bloom_batch.stopping_criterias[0].max_new_tokens
+    )
+
+    next_batch = next_batch.filter([next_batch.requests[1]])
+
+    for _ in range(
+        default_multi_requests_bloom_batch.stopping_criterias[0].max_new_tokens
+        - default_bloom_batch.stopping_criterias[0].max_new_tokens
+        - default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
+        - 4
+    ):
+        generations, next_batch = default_bloom.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_bloom.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert (
+        generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
+    )
+    assert (
+        generations[0].request_id == default_multi_requests_bloom_batch.requests[0].id
+    )
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_multi_requests_bloom_batch.stopping_criterias[0].max_new_tokens
+    )
diff --git a/server/tests/models/test_causal_lm.py b/server/tests/models/test_causal_lm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1f13e4b8db4689925faee74330842ba4cb9f85f
--- /dev/null
+++ b/server/tests/models/test_causal_lm.py
@@ -0,0 +1,332 @@
+import pytest
+import torch
+
+from copy import copy
+from transformers import AutoTokenizer
+
+from text_generation_server.pb import generate_pb2
+from text_generation_server.models.causal_lm import CausalLM, CausalLMBatch
+
+
+@pytest.fixture(scope="session")
+def default_causal_lm():
+    return CausalLM("gpt2")
+
+
+@pytest.fixture(scope="session")
+def gpt2_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left")
+    tokenizer.pad_token_id = 50256
+    return tokenizer
+
+
+@pytest.fixture
+def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
+    return generate_pb2.Request(
+        id=0,
+        inputs="Test",
+        truncate=100,
+        parameters=default_pb_parameters,
+        stopping_parameters=default_pb_stop_parameters,
+    )
+
+
+@pytest.fixture
+def default_pb_batch(default_pb_request):
+    return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
+
+
+@pytest.fixture
+def default_causal_lm_batch(default_pb_batch, gpt2_tokenizer):
+    return CausalLMBatch.from_pb(default_pb_batch, gpt2_tokenizer, torch.device("cpu"))
+
+
+@pytest.fixture
+def default_multi_requests_causal_lm_batch(default_pb_request, gpt2_tokenizer):
+    req_0 = copy(default_pb_request)
+    req_0.id = 1
+    req_1 = default_pb_request
+    req_1.id = 2
+    req_1.stopping_parameters.max_new_tokens = 5
+
+    batch_pb = generate_pb2.Batch(id=1, requests=[req_0, req_1], size=2)
+    return CausalLMBatch.from_pb(batch_pb, gpt2_tokenizer, torch.device("cpu"))
+
+
+def test_batch_from_pb(default_pb_batch, default_causal_lm_batch):
+    batch = default_causal_lm_batch
+
+    assert batch.batch_id == default_pb_batch.id
+    assert batch.requests == default_pb_batch.requests
+
+    assert len(batch.input_ids) == default_pb_batch.size
+    assert batch.input_ids[0][-1] == 14402
+    assert torch.all(batch.input_ids[0][:-1] == 50256)
+
+    assert batch.attention_mask[0, 0] == 1
+    assert torch.all(batch.attention_mask[0, 1:] == 0)
+
+    assert batch.past_key_values is None
+
+    assert all(
+        [
+            torch.equal(input_ids, all_input_ids[:, 0])
+            for input_ids, all_input_ids in zip(batch.input_ids, batch.all_input_ids)
+        ]
+    )
+
+    assert batch.input_lengths == [1]
+
+    assert len(batch) == default_pb_batch.size
+    assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
+
+    assert batch.max_input_length == batch.input_lengths[0]
+
+
+def test_batch_concatenate_no_prefill(default_causal_lm_batch):
+    with pytest.raises(ValueError):
+        CausalLMBatch.concatenate([default_causal_lm_batch, default_causal_lm_batch])
+
+
+def test_causal_lm_batch_type(default_causal_lm):
+    assert default_causal_lm.batch_type == CausalLMBatch
+
+
+def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
+    sequence_length = len(default_causal_lm_batch.all_input_ids[0])
+    generations, next_batch = default_causal_lm.generate_token(default_causal_lm_batch)
+
+    assert len(generations) == len(next_batch)
+    assert isinstance(next_batch, CausalLMBatch)
+
+    assert len(next_batch.all_input_ids) == len(next_batch)
+    assert len(next_batch.all_input_ids[0]) == sequence_length + 1
+    assert len(next_batch.attention_mask[0]) == 11
+    assert next_batch.all_input_ids[0][-1] == 13
+    assert next_batch.all_input_ids[0][-2] == 14402
+    assert torch.all(next_batch.all_input_ids[0][:-2] == 50256)
+
+    assert torch.all(next_batch.attention_mask[0][0:2] == 1)
+    assert torch.all(next_batch.attention_mask[0][2:] == 0)
+
+    assert next_batch.input_ids.shape == (len(next_batch), 1)
+    assert next_batch.input_ids[0, 0] == 13
+
+    assert next_batch.input_lengths == [2]
+    assert next_batch.max_input_length == next_batch.input_lengths[0]
+
+    assert next_batch.past_key_values is not None
+    assert all(
+        [p[0].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[1].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
+    )
+    assert all([generation.generated_text is None for generation in generations])
+    assert all([len(generation.prefill_tokens) == 1 for generation in generations])
+    assert all([generation.token_id.item() == 13 for generation in generations])
+    assert all([generation.token_text == "." for generation in generations])
+    assert generations[0].request_id == 0
+
+
+def test_causal_lm_generate_token_completion(
+    default_causal_lm, default_causal_lm_batch
+):
+    next_batch = default_causal_lm_batch
+    for _ in range(default_causal_lm_batch.stopping_criterias[0].max_new_tokens - 1):
+        generations, next_batch = default_causal_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_causal_lm.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
+    assert generations[0].request_id == default_causal_lm_batch.requests[0].id
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_causal_lm_batch.stopping_criterias[0].max_new_tokens
+    )
+
+
+def test_causal_lm_generate_token_completion_multi(
+    default_causal_lm, default_multi_requests_causal_lm_batch
+):
+    next_batch = default_multi_requests_causal_lm_batch
+
+    for i in range(
+        default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens - 1
+    ):
+        generations, next_batch = default_causal_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_causal_lm.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 2
+    assert generations[1].generated_text.text == ".java:784)"
+    assert (
+        generations[1].request_id
+        == default_multi_requests_causal_lm_batch.requests[1].id
+    )
+    assert (
+        generations[1].generated_text.generated_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
+    )
+    # Copy stopping_criterias before filtering
+    stopping_criterias = (
+        default_multi_requests_causal_lm_batch.stopping_criterias.copy()
+    )
+
+    next_batch = next_batch.filter([next_batch.requests[0]])
+
+    for _ in range(
+        stopping_criterias[0].max_new_tokens - stopping_criterias[1].max_new_tokens - 1
+    ):
+        generations, next_batch = default_causal_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_causal_lm.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
+    assert (
+        generations[0].request_id
+        == default_multi_requests_causal_lm_batch.requests[0].id
+    )
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
+    )
+
+
+def test_batch_concatenate(
+    default_causal_lm, default_causal_lm_batch, default_multi_requests_causal_lm_batch
+):
+    next_batch_0 = default_causal_lm_batch
+    _, next_batch_0 = default_causal_lm.generate_token(next_batch_0)
+    _, next_batch_0 = default_causal_lm.generate_token(next_batch_0)
+
+    next_batch_1 = default_multi_requests_causal_lm_batch
+    _, next_batch_1 = default_causal_lm.generate_token(next_batch_1)
+
+    # Clone past_key_values before concatenating to compare after,
+    # because they are removed from the concatenated batches
+    next_batch_0_past_key_values = [
+        (k.clone(), v.clone()) for (k, v) in next_batch_0.past_key_values
+    ]
+    next_batch_1_past_key_values = [
+        (k.clone(), v.clone()) for (k, v) in next_batch_1.past_key_values
+    ]
+
+    next_batch = CausalLMBatch.concatenate([next_batch_0, next_batch_1])
+
+    assert torch.equal(next_batch.all_input_ids[0], next_batch_0.all_input_ids[0])
+    assert torch.equal(next_batch.all_input_ids[1], next_batch_1.all_input_ids[0])
+    assert torch.equal(next_batch.all_input_ids[2], next_batch_1.all_input_ids[1])
+
+    assert torch.all(
+        next_batch.attention_mask[0, : -next_batch.padding_right_offset] == 1
+    )
+    assert torch.all(
+        next_batch.attention_mask[1:, 1 : -next_batch.padding_right_offset] == 1
+    )
+    assert torch.all(next_batch.attention_mask[1:, 3:] == 0)
+
+    assert next_batch.batch_id == 0
+    assert next_batch.input_ids[0, 0] == 12355
+    assert torch.all(next_batch.input_ids[1:] == 13)
+
+    assert next_batch.input_lengths == [3, 2, 2]
+    assert next_batch.max_input_length == 3
+
+    assert next_batch.requests[0] == next_batch_0.requests[0]
+    assert next_batch.requests[1:] == next_batch_1.requests
+
+    assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0]
+    assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers
+
+    assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0]
+    assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias
+
+    assert next_batch.past_key_values is not None
+    assert all([p[0].shape == (3, 12, 2, 64) for p in next_batch.past_key_values])
+    assert all([p[1].shape == (3, 12, 2, 64) for p in next_batch.past_key_values])
+
+    for i, past in enumerate(next_batch.past_key_values):
+        assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:], past[0][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][0][:, :, -1:], past[0][1:, :, -1:, :]
+        )
+
+        assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:], past[1][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][1][:, :, -1:], past[1][1:, :, -1:, :]
+        )
+
+    for _ in range(
+        default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens - 2
+    ):
+        generations, next_batch = default_causal_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_causal_lm.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 3
+    assert generations[2].generated_text.text == ".java:784)"
+    assert (
+        generations[2].request_id
+        == default_multi_requests_causal_lm_batch.requests[1].id
+    )
+    assert (
+        generations[2].generated_text.generated_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
+    )
+
+    next_batch = next_batch.filter([next_batch.requests[0], next_batch.requests[1]])
+
+    for _ in range(
+        default_causal_lm_batch.stopping_criterias[0].max_new_tokens
+        - default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
+        - 2
+    ):
+        generations, next_batch = default_causal_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_causal_lm.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 2
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
+    assert generations[0].request_id == default_causal_lm_batch.requests[0].id
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_causal_lm_batch.stopping_criterias[0].max_new_tokens
+    )
+
+    next_batch = next_batch.filter([next_batch.requests[1]])
+
+    for _ in range(
+        default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
+        - default_causal_lm_batch.stopping_criterias[0].max_new_tokens
+        - default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
+        - 4
+    ):
+        generations, next_batch = default_causal_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_causal_lm.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
+    assert (
+        generations[0].request_id
+        == default_multi_requests_causal_lm_batch.requests[0].id
+    )
+    assert (
+        generations[0].generated_text.generated_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
+    )
diff --git a/server/tests/models/test_santacoder.py b/server/tests/models/test_santacoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cf66d47783ae82d01fd78bbadb3d23a52dd89f0
--- /dev/null
+++ b/server/tests/models/test_santacoder.py
@@ -0,0 +1,93 @@
+import pytest
+
+from text_generation_server.pb import generate_pb2
+from text_generation_server.models.causal_lm import CausalLMBatch
+from text_generation_server.models.santacoder import SantaCoder
+
+
+@pytest.fixture(scope="session")
+def default_santacoder():
+    return SantaCoder("bigcode/santacoder")
+
+
+@pytest.fixture
+def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
+    return generate_pb2.Request(
+        id=0,
+        inputs="def",
+        truncate=100,
+        parameters=default_pb_parameters,
+        stopping_parameters=default_pb_stop_parameters,
+    )
+
+
+@pytest.fixture
+def default_pb_batch(default_pb_request):
+    return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
+
+
+@pytest.fixture
+def default_fim_pb_request(default_pb_parameters, default_pb_stop_parameters):
+    return generate_pb2.Request(
+        id=0,
+        inputs="<fim-prefix>def<fim-suffix>world<fim-middle>",
+        truncate=100,
+        parameters=default_pb_parameters,
+        stopping_parameters=default_pb_stop_parameters,
+    )
+
+
+@pytest.fixture
+def default_fim_pb_batch(default_fim_pb_request):
+    return generate_pb2.Batch(id=0, requests=[default_fim_pb_request], size=1)
+
+
+@pytest.mark.skip
+def test_santacoder_generate_token_completion(default_santacoder, default_pb_batch):
+    batch = CausalLMBatch.from_pb(
+        default_pb_batch, default_santacoder.tokenizer, default_santacoder.device
+    )
+    next_batch = batch
+
+    for _ in range(batch.stopping_criterias[0].max_new_tokens - 1):
+        generations, next_batch = default_santacoder.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_santacoder.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert generations[0].generated_text.text == " test_get_all_users_with_"
+    assert generations[0].request_id == batch.requests[0].id
+    assert (
+        generations[0].generated_text.generated_tokens
+        == batch.stopping_criterias[0].max_new_tokens
+    )
+
+
+@pytest.mark.skip
+def test_fim_santacoder_generate_token_completion(
+    default_santacoder, default_fim_pb_batch
+):
+    batch = CausalLMBatch.from_pb(
+        default_fim_pb_batch, default_santacoder.tokenizer, default_santacoder.device
+    )
+    next_batch = batch
+
+    for _ in range(batch.stopping_criterias[0].max_new_tokens - 1):
+        generations, next_batch = default_santacoder.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_santacoder.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert (
+        generations[0].generated_text.text
+        == """ineProperty(exports, "__esModule", { value"""
+    )
+    assert generations[0].request_id == batch.requests[0].id
+    assert (
+        generations[0].generated_text.generated_tokens
+        == batch.stopping_criterias[0].max_new_tokens
+    )
diff --git a/server/tests/models/test_seq2seq_lm.py b/server/tests/models/test_seq2seq_lm.py
new file mode 100644
index 0000000000000000000000000000000000000000..65dafa507ffce20cd6511a63af85986294dd54d8
--- /dev/null
+++ b/server/tests/models/test_seq2seq_lm.py
@@ -0,0 +1,347 @@
+import pytest
+import torch
+
+from copy import copy
+
+from transformers import AutoTokenizer
+
+from text_generation_server.pb import generate_pb2
+from text_generation_server.models.seq2seq_lm import Seq2SeqLM, Seq2SeqLMBatch
+
+
+@pytest.fixture(scope="session")
+def mt0_small_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained(
+        "bigscience/mt0-small", padding_side="left"
+    )
+    tokenizer.bos_token_id = 0
+    return tokenizer
+
+
+@pytest.fixture(scope="session")
+def default_seq2seq_lm():
+    return Seq2SeqLM("bigscience/mt0-small")
+
+
+@pytest.fixture
+def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
+    return generate_pb2.Request(
+        id=0,
+        inputs="Test",
+        truncate=100,
+        parameters=default_pb_parameters,
+        stopping_parameters=default_pb_stop_parameters,
+    )
+
+
+@pytest.fixture
+def default_pb_batch(default_pb_request):
+    return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
+
+
+@pytest.fixture
+def default_seq2seq_lm_batch(default_pb_batch, mt0_small_tokenizer):
+    return Seq2SeqLMBatch.from_pb(
+        default_pb_batch, mt0_small_tokenizer, torch.device("cpu")
+    )
+
+
+@pytest.fixture
+def default_multi_requests_seq2seq_lm_batch(default_pb_request, mt0_small_tokenizer):
+    req_0 = copy(default_pb_request)
+    req_0.id = 1
+    req_1 = default_pb_request
+    req_1.id = 2
+    req_1.stopping_parameters.max_new_tokens = 5
+
+    batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
+    return Seq2SeqLMBatch.from_pb(batch_pb, mt0_small_tokenizer, torch.device("cpu"))
+
+
+def test_batch_from_pb(default_pb_batch, default_seq2seq_lm_batch):
+    batch = default_seq2seq_lm_batch
+    sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
+
+    assert batch.batch_id == default_pb_batch.id
+    assert batch.requests == default_pb_batch.requests
+
+    assert batch.input_ids.shape == (default_pb_batch.size, sequence_length)
+    assert batch.input_ids[0][-2] == 4268
+    assert batch.input_ids[0][-1] == 1
+    assert torch.all(batch.input_ids[0][:-2] == 0)
+
+    assert torch.all(batch.attention_mask[0][-2:] == 1)
+    assert torch.all(batch.attention_mask[0][:-2] == 0)
+
+    assert len(batch.decoder_input_ids) == default_pb_batch.size
+    assert batch.decoder_attention_mask is None
+    assert batch.encoder_last_hidden_state is None
+
+    assert batch.past_key_values is None
+
+    assert batch.input_lengths == [2]
+    assert batch.decoder_input_lengths == [1]
+
+    assert len(batch) == default_pb_batch.size
+    assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
+
+    assert batch.max_input_length == batch.input_lengths[0]
+    assert batch.max_decoder_input_length == batch.decoder_input_lengths[0]
+
+
+def test_batch_concatenate_no_prefill(default_seq2seq_lm_batch):
+    with pytest.raises(ValueError):
+        Seq2SeqLMBatch.concatenate([default_seq2seq_lm_batch, default_seq2seq_lm_batch])
+
+
+def test_seq2seq_lm_batch_type(default_seq2seq_lm):
+    assert default_seq2seq_lm.batch_type == Seq2SeqLMBatch
+
+
+def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch):
+    sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
+    generations, next_batch = default_seq2seq_lm.generate_token(
+        default_seq2seq_lm_batch
+    )
+
+    assert len(generations) == len(next_batch)
+    assert isinstance(next_batch, Seq2SeqLMBatch)
+
+    assert next_batch.input_ids is None
+    assert torch.equal(
+        next_batch.attention_mask, default_seq2seq_lm_batch.attention_mask
+    )
+    assert next_batch.input_lengths == default_seq2seq_lm_batch.input_lengths
+    assert next_batch.max_input_length == default_seq2seq_lm_batch.max_input_length
+    assert (
+        next_batch.next_token_choosers == default_seq2seq_lm_batch.next_token_choosers
+    )
+    assert next_batch.stopping_criterias == default_seq2seq_lm_batch.stopping_criterias
+
+    assert len(next_batch.decoder_input_ids) == len(next_batch)
+    assert next_batch.all_decoder_input_ids[0][0] == 0
+    assert next_batch.all_decoder_input_ids[0][1] == 259
+    assert next_batch.decoder_attention_mask is None
+    assert next_batch.encoder_last_hidden_state.shape == (1, sequence_length, 512)
+
+    assert next_batch.decoder_input_lengths == [2]
+    assert next_batch.max_decoder_input_length == 2
+
+    assert next_batch.past_key_values is not None
+    assert all(
+        [p[0].shape == (len(next_batch), 6, 1, 64) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[1].shape == (len(next_batch), 6, 1, 64) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [
+            p[2].shape == (len(next_batch), 6, sequence_length, 64)
+            for p in next_batch.past_key_values
+        ]
+    )
+    assert all(
+        [
+            p[3].shape == (len(next_batch), 6, sequence_length, 64)
+            for p in next_batch.past_key_values
+        ]
+    )
+    assert all([generation.generated_text is None for generation in generations])
+    assert all([len(generation.prefill_tokens) == 1 for generation in generations])
+    assert all([generation.token_id.item() == 259 for generation in generations])
+    assert all([generation.token_text == "" for generation in generations])
+    assert generations[0].request_id == 0
+
+
+def test_seq2seq_lm_generate_token_completion(
+    default_seq2seq_lm, default_seq2seq_lm_batch
+):
+    next_batch = default_seq2seq_lm_batch
+    for _ in range(6):
+        generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert generations[0].generated_text.text == "a few weeks"
+    assert generations[0].request_id == default_seq2seq_lm_batch.requests[0].id
+    assert generations[0].generated_text.generated_tokens == 7
+
+
+def test_seq2seq_lm_generate_token_completion_multi(
+    default_seq2seq_lm, default_multi_requests_seq2seq_lm_batch
+):
+    next_batch = default_multi_requests_seq2seq_lm_batch
+
+    for i in range(4):
+        generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 2
+    assert generations[1].generated_text.text == "a few "
+    assert (
+        generations[1].request_id
+        == default_multi_requests_seq2seq_lm_batch.requests[1].id
+    )
+    assert generations[1].generated_text.generated_tokens == 5
+
+    next_batch = next_batch.filter([next_batch.requests[0]])
+
+    generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+    assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert generations[0].generated_text.text == "a few weeks"
+    assert (
+        generations[0].request_id
+        == default_multi_requests_seq2seq_lm_batch.requests[0].id
+    )
+    assert generations[0].generated_text.generated_tokens == 7
+
+
+def test_batch_concatenate(
+    default_seq2seq_lm,
+    default_seq2seq_lm_batch,
+    default_multi_requests_seq2seq_lm_batch,
+):
+    next_batch_0 = default_seq2seq_lm_batch
+    _, next_batch_0 = default_seq2seq_lm.generate_token(next_batch_0)
+    _, next_batch_0 = default_seq2seq_lm.generate_token(next_batch_0)
+
+    next_batch_1 = default_multi_requests_seq2seq_lm_batch
+    _, next_batch_1 = default_seq2seq_lm.generate_token(next_batch_1)
+
+    # Copy hidden state because it is removed from the concatenated branches
+    next_batch_0_encoder_last_hidden_state = next_batch_0.encoder_last_hidden_state
+    next_batch_1_encoder_last_hidden_state = next_batch_1.encoder_last_hidden_state
+
+    # Clone past_key_values before concatenating to compare after,
+    # because they are removed from the concatenated batches
+    next_batch_0_past_key_values = [
+        [t.clone() for t in layer] for layer in next_batch_0.past_key_values
+    ]
+    next_batch_1_past_key_values = [
+        [t.clone() for t in layer] for layer in next_batch_1.past_key_values
+    ]
+
+    next_batch = Seq2SeqLMBatch.concatenate([next_batch_0, next_batch_1])
+
+    assert next_batch.batch_id == 0
+
+    assert torch.equal(
+        next_batch.decoder_input_ids[0], next_batch_0.decoder_input_ids[0]
+    )
+    assert next_batch.all_decoder_input_ids[1][0] == 0
+    assert next_batch.all_decoder_input_ids[2][0] == 0
+    assert torch.equal(
+        next_batch.decoder_input_ids[1:, -2:], next_batch_1.decoder_input_ids
+    )
+
+    assert torch.all(next_batch.decoder_attention_mask[0, :3] == 1)
+    assert torch.all(next_batch.decoder_attention_mask[0, 3:] == 0)
+    assert torch.all(next_batch.decoder_attention_mask[1:, 0] == 0)
+    assert torch.all(next_batch.decoder_attention_mask[1:, 1:3] == 1)
+
+    assert torch.equal(
+        next_batch.encoder_last_hidden_state[0],
+        next_batch_0_encoder_last_hidden_state[0, -2:],
+    )
+    assert torch.equal(
+        next_batch.encoder_last_hidden_state[1:],
+        next_batch_1_encoder_last_hidden_state[:, -2:],
+    )
+
+    assert next_batch.input_lengths == [2, 2, 2]
+    assert next_batch.decoder_input_lengths == [3, 2, 2]
+    assert next_batch.max_input_length == 2
+    assert next_batch.max_decoder_input_length == 3
+
+    assert next_batch.requests[0] == next_batch_0.requests[0]
+    assert next_batch.requests[1:] == next_batch_1.requests
+
+    assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0]
+    assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers
+
+    assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0]
+    assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias
+
+    assert next_batch.past_key_values is not None
+    assert all(
+        [p[0].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[1].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[2].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
+    )
+    assert all(
+        [p[3].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
+    )
+
+    for i, past in enumerate(next_batch.past_key_values):
+        assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:, :], past[0][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][0][:, :, -1:, :], past[0][1:, :, -1:, :]
+        )
+
+        assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:, :], past[1][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][1][:, :, -1:, :], past[1][1:, :, -1:, :]
+        )
+
+        assert torch.equal(next_batch_0_past_key_values[i][2][0, :, -2:, :], past[2][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][2][:, :, -2:, :], past[2][1:]
+        )
+
+        assert torch.equal(next_batch_0_past_key_values[i][3][0, :, -2:, :], past[3][0])
+        assert torch.equal(
+            next_batch_1_past_key_values[i][3][:, :, -2:, :], past[3][1:]
+        )
+
+    for _ in range(3):
+        generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+        assert len(generations) == len(next_batch)
+
+    generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 3
+    assert generations[2].generated_text.text == "a few "
+    assert (
+        generations[2].request_id
+        == default_multi_requests_seq2seq_lm_batch.requests[1].id
+    )
+    assert generations[2].generated_text.generated_tokens == 5
+
+    next_batch = next_batch.filter([next_batch.requests[0], next_batch.requests[1]])
+
+    generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+    assert next_batch is not None
+
+    assert len(generations) == 2
+    assert generations[0].generated_text.text == "a few weeks"
+    assert generations[0].request_id == default_seq2seq_lm_batch.requests[0].id
+    assert generations[0].generated_text.generated_tokens == 7
+
+    next_batch = next_batch.filter([next_batch.requests[1]])
+
+    generations, next_batch = default_seq2seq_lm.generate_token(next_batch)
+    assert next_batch is None
+
+    assert len(generations) == 1
+    assert generations[0].generated_text.text == "a few weeks"
+    assert (
+        generations[0].request_id
+        == default_multi_requests_seq2seq_lm_batch.requests[0].id
+    )
+    assert generations[0].generated_text.generated_tokens == 7
diff --git a/server/tests/utils/test_convert.py b/server/tests/utils/test_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dfe6a1e33c8515a6226ea08d8649ee579d3d8f8
--- /dev/null
+++ b/server/tests/utils/test_convert.py
@@ -0,0 +1,21 @@
+from text_generation_server.utils.hub import (
+    download_weights,
+    weight_hub_files,
+    weight_files,
+)
+
+from text_generation_server.utils.convert import convert_files
+
+
+def test_convert_files():
+    model_id = "bigscience/bloom-560m"
+    pt_filenames = weight_hub_files(model_id, extension=".bin")
+    local_pt_files = download_weights(pt_filenames, model_id)
+    local_st_files = [
+        p.parent / f"{p.stem.lstrip('pytorch_')}.safetensors" for p in local_pt_files
+    ]
+    convert_files(local_pt_files, local_st_files)
+
+    found_st_files = weight_files(model_id)
+
+    assert all([p in found_st_files for p in local_st_files])
diff --git a/server/tests/utils/test_hub.py b/server/tests/utils/test_hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..fac9a64d565771283c77d7ebde434c7ef6418367
--- /dev/null
+++ b/server/tests/utils/test_hub.py
@@ -0,0 +1,40 @@
+import pytest
+
+from text_generation_server.utils.hub import (
+    weight_hub_files,
+    download_weights,
+    weight_files,
+    EntryNotFoundError,
+    LocalEntryNotFoundError,
+    RevisionNotFoundError,
+)
+
+
+def test_weight_hub_files():
+    filenames = weight_hub_files("bigscience/bloom-560m")
+    assert filenames == ["model.safetensors"]
+
+
+def test_weight_hub_files_llm():
+    filenames = weight_hub_files("bigscience/bloom")
+    assert filenames == [f"model_{i:05d}-of-00072.safetensors" for i in range(1, 73)]
+
+
+def test_weight_hub_files_empty():
+    with pytest.raises(EntryNotFoundError):
+        weight_hub_files("bigscience/bloom", extension=".errors")
+
+
+def test_download_weights():
+    model_id = "bigscience/bloom-560m"
+    filenames = weight_hub_files(model_id)
+    files = download_weights(filenames, model_id)
+    local_files = weight_files("bigscience/bloom-560m")
+    assert files == local_files
+
+
+def test_weight_files_error():
+    with pytest.raises(RevisionNotFoundError):
+        weight_files("bigscience/bloom-560m", revision="error")
+    with pytest.raises(LocalEntryNotFoundError):
+        weight_files("bert-base-uncased")
diff --git a/server/tests/utils/test_tokens.py b/server/tests/utils/test_tokens.py
new file mode 100644
index 0000000000000000000000000000000000000000..da0006e462d7a2983e303278c7de0219eb55dc8c
--- /dev/null
+++ b/server/tests/utils/test_tokens.py
@@ -0,0 +1,44 @@
+from text_generation_server.utils.tokens import (
+    StopSequenceCriteria,
+    StoppingCriteria,
+    FinishReason,
+)
+
+
+def test_stop_sequence_criteria():
+    criteria = StopSequenceCriteria("/test;")
+
+    assert not criteria("/")
+    assert not criteria("/test")
+    assert criteria("/test;")
+    assert not criteria("/test; ")
+
+
+def test_stop_sequence_criteria_escape():
+    criteria = StopSequenceCriteria("<|stop|>")
+
+    assert not criteria("<")
+    assert not criteria("<|stop")
+    assert criteria("<|stop|>")
+    assert not criteria("<|stop|> ")
+
+
+def test_stopping_criteria():
+    criteria = StoppingCriteria(0, [StopSequenceCriteria("/test;")], max_new_tokens=5)
+    assert criteria(65827, "/test") == (False, None)
+    assert criteria(30, ";") == (True, FinishReason.FINISH_REASON_STOP_SEQUENCE)
+
+
+def test_stopping_criteria_eos():
+    criteria = StoppingCriteria(0, [StopSequenceCriteria("/test;")], max_new_tokens=5)
+    assert criteria(1, "") == (False, None)
+    assert criteria(0, "") == (True, FinishReason.FINISH_REASON_EOS_TOKEN)
+
+
+def test_stopping_criteria_max():
+    criteria = StoppingCriteria(0, [StopSequenceCriteria("/test;")], max_new_tokens=5)
+    assert criteria(1, "") == (False, None)
+    assert criteria(1, "") == (False, None)
+    assert criteria(1, "") == (False, None)
+    assert criteria(1, "") == (False, None)
+    assert criteria(1, "") == (True, FinishReason.FINISH_REASON_LENGTH)
diff --git a/server/tests/utils/test_watermark.py b/server/tests/utils/test_watermark.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d0aaf05006f908ce057e339d76fb5581ace3dcd
--- /dev/null
+++ b/server/tests/utils/test_watermark.py
@@ -0,0 +1,54 @@
+# test_watermark_logits_processor.py
+
+import os
+import numpy as np
+import torch
+from text_generation_server.utils.watermark import WatermarkLogitsProcessor
+
+
+GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
+DELTA = os.getenv("WATERMARK_DELTA", 2.0)
+
+
+def test_seed_rng():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    processor._seed_rng(input_ids)
+    assert isinstance(processor.rng, torch.Generator)
+
+
+def test_get_greenlist_ids():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    result = processor._get_greenlist_ids(input_ids, 10, torch.device("cpu"))
+    assert max(result) <= 10
+    assert len(result) == int(10 * 0.5)
+
+
+def test_calc_greenlist_mask():
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    greenlist_token_ids = torch.tensor([2, 3])
+    result = processor._calc_greenlist_mask(scores, greenlist_token_ids)
+    assert result.tolist() == [[False, False, False, False], [False, False, True, True]]
+    assert result.shape == scores.shape
+
+
+def test_bias_greenlist_logits():
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    green_tokens_mask = torch.tensor(
+        [[False, False, True, True], [False, False, False, True]]
+    )
+    greenlist_bias = 2.0
+    result = processor._bias_greenlist_logits(scores, green_tokens_mask, greenlist_bias)
+    assert np.allclose(result.tolist(), [[0.5, 0.3, 2.2, 2.8], [0.1, 0.2, 0.7, 2.9]])
+    assert result.shape == scores.shape
+
+
+def test_call():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    result = processor(input_ids, scores)
+    assert result.shape == scores.shape
diff --git a/server/text_generation_server/__init__.py b/server/text_generation_server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/server/text_generation_server/cache.py b/server/text_generation_server/cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..5556529c3b6093e8ed490262ce15a42d4ff897d3
--- /dev/null
+++ b/server/text_generation_server/cache.py
@@ -0,0 +1,28 @@
+from typing import Dict, Optional, TypeVar
+
+from text_generation_server.models.types import Batch
+
+B = TypeVar("B", bound=Batch)
+
+
+class Cache:
+    def __init__(self):
+        self.cache: Dict[int, B] = {}
+
+    def pop(self, batch_id: int) -> Optional[B]:
+        return self.cache.pop(batch_id, None)
+
+    def set(self, entry: B):
+        if entry is not None:
+            self.cache[entry.batch_id] = entry
+
+    def delete(self, batch_id: int):
+        batch = self.pop(batch_id)
+        if batch is not None:
+            del batch
+
+    def clear(self):
+        self.cache.clear()
+
+    def __len__(self):
+        return len(self.cache.keys())
diff --git a/server/text_generation_server/cli.py b/server/text_generation_server/cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..94340facfd3e3f8acf0d2d86bbaee804c83b8483
--- /dev/null
+++ b/server/text_generation_server/cli.py
@@ -0,0 +1,121 @@
+import os
+import sys
+import typer
+
+from pathlib import Path
+from loguru import logger
+from typing import Optional
+
+
+app = typer.Typer()
+
+
+@app.command()
+def serve(
+    model_id: str,
+    revision: Optional[str] = None,
+    sharded: bool = False,
+    quantize: bool = False,
+    uds_path: Path = "/tmp/text-generation-server",
+    logger_level: str = "INFO",
+    json_output: bool = False,
+    otlp_endpoint: Optional[str] = None,
+):
+    if sharded:
+        assert (
+            os.getenv("RANK", None) is not None
+        ), "RANK must be set when sharded is True"
+        assert (
+            os.getenv("WORLD_SIZE", None) is not None
+        ), "WORLD_SIZE must be set when sharded is True"
+        assert (
+            os.getenv("MASTER_ADDR", None) is not None
+        ), "MASTER_ADDR must be set when sharded is True"
+        assert (
+            os.getenv("MASTER_PORT", None) is not None
+        ), "MASTER_PORT must be set when sharded is True"
+
+    # Remove default handler
+    logger.remove()
+    logger.add(
+        sys.stdout,
+        format="{message}",
+        filter="text_generation_server",
+        level=logger_level,
+        serialize=json_output,
+        backtrace=True,
+        diagnose=False,
+    )
+
+    # Import here after the logger is added to log potential import exceptions
+    from text_generation_server import server
+    from text_generation_server.tracing import setup_tracing
+
+    # Setup OpenTelemetry distributed tracing
+    if otlp_endpoint is not None:
+        setup_tracing(shard=os.getenv("RANK", 0), otlp_endpoint=otlp_endpoint)
+
+    server.serve(model_id, revision, sharded, quantize, uds_path)
+
+
+@app.command()
+def download_weights(
+    model_id: str,
+    revision: Optional[str] = None,
+    extension: str = ".safetensors",
+    logger_level: str = "INFO",
+    json_output: bool = False,
+):
+    # Remove default handler
+    logger.remove()
+    logger.add(
+        sys.stdout,
+        format="{message}",
+        filter="text_generation_server",
+        level=logger_level,
+        serialize=json_output,
+        backtrace=True,
+        diagnose=False,
+    )
+
+    # Import here after the logger is added to log potential import exceptions
+    from text_generation_server import utils
+
+    # Test if files were already download
+    try:
+        utils.weight_files(model_id, revision, extension)
+        logger.info(
+            "Files are already present in the local cache. " "Skipping download."
+        )
+        return
+    # Local files not found
+    except utils.LocalEntryNotFoundError:
+        pass
+
+    # Download weights directly
+    try:
+        filenames = utils.weight_hub_files(model_id, revision, extension)
+        utils.download_weights(filenames, model_id, revision)
+    except utils.EntryNotFoundError as e:
+        if not extension == ".safetensors":
+            raise e
+
+        logger.warning(
+            f"No safetensors weights found for model {model_id} at revision {revision}. "
+            f"Converting PyTorch weights instead."
+        )
+
+        # Try to see if there are pytorch weights
+        pt_filenames = utils.weight_hub_files(model_id, revision, ".bin")
+        # Download pytorch weights
+        local_pt_files = utils.download_weights(pt_filenames, model_id, revision)
+        local_st_files = [
+            p.parent / f"{p.stem.lstrip('pytorch_')}.safetensors"
+            for p in local_pt_files
+        ]
+        # Convert pytorch weights to safetensors
+        utils.convert_files(local_pt_files, local_st_files)
+
+
+if __name__ == "__main__":
+    app()
diff --git a/server/text_generation_server/interceptor.py b/server/text_generation_server/interceptor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3247d197326eec1d60ab2884a25cbd2ee02954d
--- /dev/null
+++ b/server/text_generation_server/interceptor.py
@@ -0,0 +1,29 @@
+import grpc
+
+from google.rpc import status_pb2, code_pb2
+from grpc_status import rpc_status
+from grpc_interceptor.server import AsyncServerInterceptor
+from loguru import logger
+from typing import Callable, Any
+
+
+class ExceptionInterceptor(AsyncServerInterceptor):
+    async def intercept(
+        self,
+        method: Callable,
+        request_or_iterator: Any,
+        context: grpc.ServicerContext,
+        method_name: str,
+    ) -> Any:
+        try:
+            response = method(request_or_iterator, context)
+            return await response
+        except Exception as err:
+            method_name = method_name.split("/")[-1]
+            logger.exception(f"Method {method_name} encountered an error.")
+
+            await context.abort_with_status(
+                rpc_status.to_status(
+                    status_pb2.Status(code=code_pb2.INTERNAL, message=str(err))
+                )
+            )
diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..221c9139d3f192c75f1806e467d91a5f6b3dfe56
--- /dev/null
+++ b/server/text_generation_server/models/__init__.py
@@ -0,0 +1,159 @@
+import torch
+
+from loguru import logger
+from transformers import AutoConfig
+from transformers.models.auto import modeling_auto
+from typing import Optional
+
+from text_generation_server.models.model import Model
+from text_generation_server.models.causal_lm import CausalLM
+from text_generation_server.models.flash_causal_lm import FlashCausalLM
+from text_generation_server.models.bloom import BLOOM, BLOOMSharded
+from text_generation_server.models.seq2seq_lm import Seq2SeqLM
+from text_generation_server.models.opt import OPT, OPTSharded
+from text_generation_server.models.galactica import Galactica, GalacticaSharded
+from text_generation_server.models.santacoder import SantaCoder
+from text_generation_server.models.gpt_neox import GPTNeoxSharded
+from text_generation_server.models.t5 import T5Sharded
+
+try:
+    if torch.cuda.is_available():
+        major, minor = torch.cuda.get_device_capability()
+        is_sm75 = major == 7 and minor == 5
+        is_sm8x = major == 8 and minor >= 0
+        is_sm90 = major == 9 and minor == 0
+
+        supported = is_sm75 or is_sm8x or is_sm90
+        if not supported:
+            raise ImportError(
+                f"GPU with CUDA capability {major} {minor} is not supported"
+            )
+
+        from text_generation_server.models.flash_neox import FlashNeoX, FlashNeoXSharded
+        from text_generation_server.models.flash_llama import (
+            FlashLlama,
+            FlashLlamaSharded,
+        )
+        from text_generation_server.models.flash_santacoder import (
+            FlashSantacoder,
+            FlashSantacoderSharded,
+        )
+
+        FLASH_ATTENTION = True
+    else:
+        FLASH_ATTENTION = False
+except ImportError:
+    logger.opt(exception=True).warning(
+        "Could not import Flash Attention enabled models"
+    )
+    FLASH_ATTENTION = False
+
+__all__ = [
+    "Model",
+    "BLOOM",
+    "BLOOMSharded",
+    "CausalLM",
+    "FlashCausalLM",
+    "Galactica",
+    "GalacticaSharded",
+    "GPTNeoxSharded",
+    "Seq2SeqLM",
+    "SantaCoder",
+    "OPT",
+    "OPTSharded",
+    "T5Sharded",
+    "get_model",
+]
+
+if FLASH_ATTENTION:
+    __all__.append(FlashNeoX)
+    __all__.append(FlashNeoXSharded)
+    __all__.append(FlashSantacoder)
+    __all__.append(FlashSantacoderSharded)
+    __all__.append(FlashLlama)
+    __all__.append(FlashLlamaSharded)
+
+FLASH_ATT_ERROR_MESSAGE = (
+    "{} requires Flash Attention CUDA kernels to be installed.\n"
+    "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
+    "or install flash attention with `cd server && make install install-flash-attention`"
+)
+
+# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
+# in PyTorch 1.12 and later.
+torch.backends.cuda.matmul.allow_tf32 = True
+
+# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
+torch.backends.cudnn.allow_tf32 = True
+
+# Disable gradients
+torch.set_grad_enabled(False)
+
+
+def get_model(
+    model_id: str, revision: Optional[str], sharded: bool, quantize: bool
+) -> Model:
+    if "facebook/galactica" in model_id:
+        if sharded:
+            return GalacticaSharded(model_id, revision, quantize=quantize)
+        else:
+            return Galactica(model_id, revision, quantize=quantize)
+
+    if "bigcode" in model_id:
+        if sharded:
+            if not FLASH_ATTENTION:
+                raise NotImplementedError(
+                    FLASH_ATT_ERROR_MESSAGE.format(f"Sharded Santacoder")
+                )
+            return FlashSantacoderSharded(model_id, revision, quantize=quantize)
+        else:
+            santacoder_cls = FlashSantacoder if FLASH_ATTENTION else SantaCoder
+            return santacoder_cls(model_id, revision, quantize=quantize)
+
+    config = AutoConfig.from_pretrained(model_id, revision=revision)
+    model_type = config.model_type
+
+    if model_type == "bloom":
+        if sharded:
+            return BLOOMSharded(model_id, revision, quantize=quantize)
+        else:
+            return BLOOM(model_id, revision, quantize=quantize)
+
+    if model_type == "gpt_neox":
+        if sharded:
+            neox_cls = FlashNeoXSharded if FLASH_ATTENTION else GPTNeoxSharded
+            return neox_cls(model_id, revision, quantize=quantize)
+        else:
+            neox_cls = FlashNeoX if FLASH_ATTENTION else CausalLM
+            return neox_cls(model_id, revision, quantize=quantize)
+
+    if model_type == "llama":
+        if sharded:
+            if FLASH_ATTENTION:
+                return FlashLlamaSharded(model_id, revision, quantize=quantize)
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format(f"Sharded Llama"))
+        else:
+            llama_cls = FlashLlama if FLASH_ATTENTION else CausalLM
+            return llama_cls(model_id, revision, quantize=quantize)
+
+    if config.model_type == "opt":
+        if sharded:
+            return OPTSharded(model_id, revision, quantize=quantize)
+        else:
+            return OPT(model_id, revision, quantize=quantize)
+
+    if model_type == "t5":
+        if sharded:
+            return T5Sharded(model_id, revision, quantize=quantize)
+        else:
+            return Seq2SeqLM(model_id, revision, quantize=quantize)
+
+    if sharded:
+        raise ValueError("sharded is not supported for AutoModel")
+
+    if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
+        return CausalLM(model_id, revision, quantize=quantize)
+    if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
+        return Seq2SeqLM(model_id, revision, quantize=quantize)
+
+    raise ValueError(f"Unsupported model type {model_type}")
diff --git a/server/text_generation_server/models/bloom.py b/server/text_generation_server/models/bloom.py
new file mode 100644
index 0000000000000000000000000000000000000000..e43a4b797018ae1532fc79f23d28f0c5ee0d2712
--- /dev/null
+++ b/server/text_generation_server/models/bloom.py
@@ -0,0 +1,242 @@
+import torch
+import torch.distributed
+
+from typing import List, Optional, Type
+
+from accelerate import init_empty_weights
+from safetensors import safe_open
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+    PreTrainedTokenizerBase,
+)
+from transformers.models.bloom.parallel_layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+)
+
+from text_generation_server.models import CausalLM
+from text_generation_server.models.causal_lm import CausalLMBatch
+from text_generation_server.pb import generate_pb2
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+)
+
+HAS_BITS_AND_BYTES = True
+try:
+    import bitsandbytes as bnb
+    from bitsandbytes.nn import Int8Params
+except Exception as e:
+    HAS_BITS_AND_BYTES = False
+
+
+class BloomCausalLMBatch(CausalLMBatch):
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        device: torch.device,
+    ) -> "CausalLMBatch":
+        batch = super(BloomCausalLMBatch, cls).from_pb(
+            pb=pb, tokenizer=tokenizer, device=device
+        )
+        batch.keys_head_dim_last = False
+        return batch
+
+
+class BLOOM(CausalLM):
+    def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
+        super(BLOOM, self).__init__(
+            model_id=model_id, revision=revision, quantize=quantize, decode_buffer=1
+        )
+
+    @property
+    def batch_type(self) -> Type[CausalLMBatch]:
+        return BloomCausalLMBatch
+
+
+class BLOOMSharded(BLOOM):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+
+        config = AutoConfig.from_pretrained(
+            model_id, revision=revision, slow_but_exact=False, tp_parallel=True
+        )
+        config.pad_token_id = 3
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = AutoModelForCausalLM.from_config(config)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+        )
+        self.model = model.eval()
+        torch.distributed.barrier(group=self.process_group)
+        super(CausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+            decode_buffer=1,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+    ):
+        parameters = dict(model.named_parameters())
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for name in f.keys():
+                    full_name = f"transformer.{name}"
+
+                    module_name, param_name = full_name.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+                    current_tensor = parameters[full_name]
+
+                    slice_ = f.get_slice(name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif isinstance(module, TensorParallelRowLinear):
+                        if param_name == "weight":
+                            size = slice_.get_shape()[1]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = slice_[:, start:stop]
+                        else:
+                            tensor = slice_[:]
+                            # XXX: Hack for Rowlinear to add the bias only once.
+                            if rank != 0:
+                                tensor = torch.zeros_like(tensor)
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    else:
+                        tensor = slice_[:]
+
+                    if current_tensor.shape != tensor.shape:
+                        raise ValueError(
+                            f"Name {name} -- Current {current_tensor.shape} and got {tensor.shape}"
+                        )
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    if quantize:
+                        if not HAS_BITS_AND_BYTES:
+                            raise ImportError(
+                                "bitsandbytes is not available on your machine either because it is not installed "
+                                "or you don't have a GPU.\n"
+                                "You can install it with `pip install bitsandbytes`."
+                            )
+
+                        if (
+                            type(module)
+                            in [TensorParallelRowLinear, TensorParallelColumnLinear]
+                            and param_name == "weight"
+                        ):
+                            tensor = Int8Params(
+                                tensor,
+                                has_fp16_weights=False,
+                                requires_grad=False,
+                            ).to(device)
+                            state = bnb.MatmulLtState()
+                            state.threshold = 6.0
+                            state.has_fp16_weights = False
+                            state.memory_efficient_backward = False
+                            state.use_pool = True
+                            state.CB = tensor.CB
+                            state.SCB = tensor.SCB
+                            tensor.CB = None
+                            tensor.SCB = None
+
+                            def replace_linear(state):
+                                def linear(input, weight, bias):
+                                    out = bnb.matmul(
+                                        input,
+                                        weight,
+                                        state=state,
+                                        threshold=state.threshold,
+                                        bias=bias,
+                                    )
+
+                                    if state.CB is not None:
+                                        # we converted 8-bit row major to turing/ampere format
+                                        # in the first inference pass
+                                        # we no longer need the row-major weight
+                                        del state.CB
+                                        weight.data = state.CxB
+
+                                    return out
+
+                                return linear
+
+                            module.linear = replace_linear(state)
+
+                        else:
+                            tensor = tensor.to(device)
+
+                    module._parameters[param_name] = tensor
+                    if name == "word_embeddings.weight":
+                        model.lm_head._parameters["weight"] = tensor
+
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+
+        # Logits are sharded, so we need to gather them
+        logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
+        torch.distributed.all_gather(logits, outputs.logits, group=self.process_group)
+        logits = torch.cat(logits, dim=2)
+
+        return logits, outputs.past_key_values
diff --git a/server/text_generation_server/models/causal_lm.py b/server/text_generation_server/models/causal_lm.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dc7fb852ba06f6b192b191932a62a9b217648a1
--- /dev/null
+++ b/server/text_generation_server/models/causal_lm.py
@@ -0,0 +1,647 @@
+import torch
+
+from dataclasses import dataclass
+from opentelemetry import trace
+from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
+from typing import Optional, Tuple, List, Type, Dict
+
+from text_generation_server.models import Model
+from text_generation_server.models.types import (
+    Batch,
+    PrefillTokens,
+    Generation,
+    GeneratedText,
+)
+from text_generation_server.pb import generate_pb2
+from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
+
+tracer = trace.get_tracer(__name__)
+
+
+@dataclass
+class CausalLMBatch(Batch):
+    batch_id: int
+    requests: List[generate_pb2.Request]
+    requests_idx_mapping: Dict[int, int]
+
+    # Decoder values
+    input_ids: torch.Tensor
+    attention_mask: torch.Tensor
+    position_ids: torch.Tensor
+    past_key_values: Optional[List[Tuple]]
+
+    # All tokens
+    all_input_ids: List[torch.Tensor]
+
+    # Lengths of all generations present in the batch
+    input_lengths: List[int]
+    offsets: List[Optional[int]]
+    token_offsets: List[Optional[int]]
+
+    # Generation helpers
+    next_token_choosers: List[NextTokenChooser]
+    stopping_criterias: List[StoppingCriteria]
+
+    # Metadata used for padding
+    max_input_length: int
+    padding_right_offset: int
+
+    # Maximum number of tokens this batch will grow to
+    max_tokens: int
+
+    # Past metadata
+    keys_head_dim_last: bool = True
+
+    def to_pb(self) -> generate_pb2.Batch:
+        return generate_pb2.Batch(
+            id=self.batch_id,
+            requests=self.requests,
+            size=len(self),
+            max_tokens=self.max_tokens,
+        )
+
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        device: torch.device,
+    ) -> "CausalLMBatch":
+        inputs = []
+        next_token_choosers = []
+        stopping_criterias = []
+        offsets = []
+        token_offsets = []
+        requests_idx_mapping = {}
+
+        # Parse batch
+        max_truncation = 0
+        padding_right_offset = 0
+        max_decode_tokens = 0
+        for i, r in enumerate(pb.requests):
+            requests_idx_mapping[r.id] = i
+            inputs.append(r.inputs)
+            offsets.append(None)
+            token_offsets.append(None)
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
+            stopping_criteria = StoppingCriteria.from_pb(
+                r.stopping_parameters, tokenizer
+            )
+            stopping_criterias.append(stopping_criteria)
+            max_truncation = max(max_truncation, r.truncate)
+            max_decode_tokens += stopping_criteria.max_new_tokens
+            padding_right_offset = max(
+                padding_right_offset, stopping_criteria.max_new_tokens
+            )
+
+        tokenized_inputs = tokenizer(
+            inputs,
+            return_tensors="pt",
+            padding=True,
+            return_token_type_ids=False,
+            truncation=True,
+            max_length=max_truncation,
+        ).to(device)
+
+        input_lengths = tokenized_inputs["attention_mask"].sum(1)
+        max_input_length = input_lengths.max()
+
+        input_ids = tokenized_inputs["input_ids"]
+        # Allocate maximum attention_mask
+        attention_mask = input_ids.new_zeros(
+            (pb.size, max_input_length + padding_right_offset)
+        )
+        # Copy tokenizer attention_mask into fully allocated attention_mask
+        attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
+
+        position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
+        position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
+        all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
+
+        max_tokens = len(inputs) * max_input_length + max_decode_tokens
+
+        return cls(
+            batch_id=pb.id,
+            requests=pb.requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=None,
+            all_input_ids=list(all_input_ids),
+            input_lengths=input_lengths.tolist(),
+            offsets=offsets,
+            token_offsets=token_offsets,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            max_input_length=max_input_length.item(),
+            padding_right_offset=padding_right_offset,
+            max_tokens=max_tokens,
+        )
+
+    @tracer.start_as_current_span("filter")
+    def filter(self, requests: List[generate_pb2.Request]) -> Optional["CausalLMBatch"]:
+        if len(requests) == 0:
+            raise ValueError("Batch must have at least one request")
+        if len(requests) == len(self):
+            return self
+
+        keep_indices = []
+
+        # New values after filtering
+        requests_idx_mapping = {}
+        input_lengths = []
+        offsets = []
+        token_offsets = []
+        all_input_ids = []
+        max_input_length = 0
+
+        next_token_choosers = []
+        stopping_criterias = []
+
+        total_remaining_decode_tokens = 0
+        new_padding_right_offset = 0
+
+        for i, r in enumerate(requests):
+            idx = self.requests_idx_mapping[r.id]
+            requests_idx_mapping[r.id] = i
+            keep_indices.append(idx)
+
+            offsets.append(self.offsets[idx])
+            token_offsets.append(self.token_offsets[idx])
+            all_input_ids.append(self.all_input_ids[idx])
+
+            request_input_length = self.input_lengths[idx]
+            input_lengths.append(request_input_length)
+            max_input_length = max(max_input_length, request_input_length)
+
+            next_token_choosers.append(self.next_token_choosers[idx])
+            stopping_criteria = self.stopping_criterias[idx]
+            stopping_criterias.append(stopping_criteria)
+            remaining_decode_tokens = (
+                stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
+            )
+            total_remaining_decode_tokens += remaining_decode_tokens
+            new_padding_right_offset = max(
+                new_padding_right_offset, remaining_decode_tokens
+            )
+
+        # Apply indices to input_ids, attention mask, past key values and other items that need to be cached
+        input_ids = self.input_ids[keep_indices]
+        position_ids = self.position_ids[keep_indices]
+        self.attention_mask = self.attention_mask[
+            keep_indices,
+            -(self.padding_right_offset + max_input_length) : (
+                self.attention_mask.shape[1] - self.padding_right_offset
+            )
+            + new_padding_right_offset,
+        ]
+
+        # Ensure that past_key_values tensors can be updated in-place
+        if type(self.past_key_values[0]) == tuple:
+            self.past_key_values = [list(layer) for layer in self.past_key_values]
+
+        # Update tensors in-place to allow incremental garbage collection
+        past_kv_length = max_input_length - 1
+        for layer in self.past_key_values:
+            past_keys, past_values = layer
+            if len(past_keys.shape) == 3:
+                # Force past to be of dim [self_size, num_heads, ...] for easy indexing
+                past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
+                past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
+            if self.keys_head_dim_last:
+                layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
+            else:
+                layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
+            del past_keys
+            layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
+            del past_values
+
+        max_tokens = len(requests) * max_input_length + total_remaining_decode_tokens
+
+        self.requests = requests
+        self.requests_idx_mapping = requests_idx_mapping
+        self.input_ids = input_ids
+        self.position_ids = position_ids
+        self.all_input_ids = all_input_ids
+        self.input_lengths = input_lengths
+        self.offsets = offsets
+        self.token_offsets = token_offsets
+        self.next_token_choosers = next_token_choosers
+        self.stopping_criterias = stopping_criterias
+        self.max_input_length = max_input_length
+        self.padding_right_offset = new_padding_right_offset
+        self.max_tokens = max_tokens
+
+        return self
+
+    @classmethod
+    @tracer.start_as_current_span("concatenate")
+    def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
+        # Used for padding
+        total_batch_size = 0
+        max_input_length = 0
+        padding_right_offset = 0
+        for batch in batches:
+            total_batch_size += len(batch)
+            max_input_length = max(max_input_length, batch.max_input_length)
+            padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
+
+        # Batch attributes
+        requests = []
+        requests_idx_mapping = {}
+        input_lengths = []
+        offsets = []
+        token_offsets = []
+        all_input_ids = []
+        next_token_choosers = []
+        stopping_criterias = []
+        max_tokens = 0
+
+        # Batch tensors
+        input_ids = None
+        attention_mask = None
+        position_ids = None
+        past_key_values = []
+
+        # Used for slicing correctly inside the tensors
+        # Equivalent to a cumsum on batch sizes
+        start_index = 0
+        for i, batch in enumerate(batches):
+            requests.extend(batch.requests)
+            input_lengths.extend(batch.input_lengths)
+            offsets.extend(batch.offsets)
+            token_offsets.extend(batch.token_offsets)
+            all_input_ids.extend(batch.all_input_ids)
+            next_token_choosers.extend(batch.next_token_choosers)
+            stopping_criterias.extend(batch.stopping_criterias)
+
+            if i == 0:
+                requests_idx_mapping = batch.requests_idx_mapping
+            else:
+                # We need to offset the mapping for each batch by the cumulative batch size
+                for k, v in batch.requests_idx_mapping.items():
+                    requests_idx_mapping[k] = v + start_index
+
+            # Slicing end index for this batch
+            end_index = start_index + len(batch)
+
+            # We only concatenate batches that did at least one step
+            if batch.past_key_values is None:
+                raise ValueError("only concatenate prefilled batches")
+
+            # Create empty tensor
+            # input_ids is always of shape [batch_size, 1]
+            # We do not need to pad it
+            if input_ids is None:
+                input_ids = batch.input_ids.new_empty((total_batch_size, 1))
+            # Copy to correct indices
+            input_ids[start_index:end_index] = batch.input_ids
+
+            # Create padded tensor
+            if attention_mask is None:
+                attention_mask = batch.attention_mask.new_zeros(
+                    (total_batch_size, max_input_length + padding_right_offset),
+                )
+
+            # We need to slice the attention mask to remove padding from previous steps
+            # and to remove unused allocated space
+            left_offset = max_input_length - batch.max_input_length
+            batch_left_offset = (
+                batch.attention_mask.shape[1]
+                - batch.max_input_length
+                - batch.padding_right_offset
+            )
+            attention_mask[
+                start_index:end_index,
+                left_offset:-padding_right_offset,
+            ] = batch.attention_mask[
+                :,
+                batch_left_offset : -batch.padding_right_offset,
+            ]
+
+            # Create empty tensor
+            # position_ids is always of shape [batch_size, 1]
+            if position_ids is None:
+                position_ids = batch.position_ids.new_empty((total_batch_size, 1))
+            position_ids[start_index:end_index] = batch.position_ids
+
+            # Shenanigans to get dimensions because BLOOM outputs a past with a different shape
+            # BLOOM Keys:   [batch_size * num_heads, head_dim, seq_length]
+            # BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
+            # And ensure that we can update tensors in-place
+            if type(batch.past_key_values[0]) == tuple:
+                batch.past_key_values = [
+                    [t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
+                    for layer in batch.past_key_values
+                ]
+            elif len(batch.past_key_values[0][0].shape) == 3:
+                for layer in batch.past_key_values:
+                    for k, t in enumerate(layer):
+                        layer[k] = t.view(len(batch), -1, *t.shape[-2:])
+
+            # Add eventual padding tokens that were added while concatenating
+            max_tokens += batch.max_tokens + (
+                max_input_length - batch.max_input_length
+            ) * len(batch)
+
+            start_index = end_index
+
+        first_past_kvs = batches[0].past_key_values
+        _, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
+
+        padded_past_values_shape = (
+            total_batch_size,
+            num_heads,
+            max_input_length - 1,
+            head_dim,
+        )
+
+        if batches[0].keys_head_dim_last:
+            padded_past_keys_shape = padded_past_values_shape
+        else:
+            # seq_length is last for BLOOM
+            padded_past_keys_shape = (
+                total_batch_size,
+                num_heads,
+                head_dim,
+                max_input_length - 1,
+            )
+
+        # Iterate over attention layers
+        # Concatenate past key values layer by layer to allow incremental garbage collection
+        for j in range(len(first_past_kvs)):
+            padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
+            start_index = 0
+            for batch in batches:
+                past_keys = batch.past_key_values[j][0]
+                # Clear reference to the original tensor
+                batch.past_key_values[j][0] = None
+
+                # Slicing end index for this batch
+                end_index = start_index + len(batch)
+                # We slice the keys to remove the padding from previous batches
+                past_seq_len = batch.max_input_length - 1
+                if batch.keys_head_dim_last:
+                    padded_past_keys[
+                        start_index:end_index, :, -past_seq_len:, :
+                    ] = past_keys[:, :, -past_seq_len:, :]
+                else:
+                    # BLOOM case
+                    padded_past_keys[
+                        start_index:end_index, :, :, -past_seq_len:
+                    ] = past_keys[:, :, :, -past_seq_len:]
+                del past_keys
+
+                start_index = end_index
+
+            padded_past_values = first_past_kvs[j][1].new_zeros(
+                padded_past_values_shape
+            )
+            start_index = 0
+            for batch in batches:
+                past_values = batch.past_key_values[j][1]
+                # Clear reference to the original tensor
+                batch.past_key_values[j][1] = None
+
+                # Slicing end index for this batch
+                end_index = start_index + len(batch)
+                # We slice the past values to remove the padding from previous batches
+                past_seq_len = batch.max_input_length - 1
+                padded_past_values[
+                    start_index:end_index, :, -past_seq_len:, :
+                ] = past_values[:, :, -past_seq_len:, :]
+                del past_values
+
+                # Update values
+                start_index = end_index
+
+            past_key_values.append([padded_past_keys, padded_past_values])
+
+        return cls(
+            batch_id=batches[0].batch_id,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            all_input_ids=all_input_ids,
+            input_lengths=input_lengths,
+            offsets=offsets,
+            token_offsets=token_offsets,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            max_input_length=max_input_length,
+            padding_right_offset=padding_right_offset,
+            keys_head_dim_last=batches[0].keys_head_dim_last,
+            max_tokens=max_tokens,
+        )
+
+    def __len__(self):
+        return len(self.requests)
+
+
+class CausalLM(Model):
+    def __init__(
+        self,
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: bool = False,
+        decode_buffer: int = 3,
+    ):
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            if quantize:
+                raise ValueError("quantization is not available on CPU")
+
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            revision=revision,
+            torch_dtype=dtype,
+            device_map="auto" if torch.cuda.is_available() else None,
+            load_in_8bit=quantize,
+        ).eval()
+        tokenizer.pad_token_id = (
+            self.model.config.pad_token_id
+            if self.model.config.pad_token_id is not None
+            else self.model.config.eos_token_id
+        )
+
+        super(CausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+            decode_buffer=decode_buffer,
+        )
+
+    @property
+    def batch_type(self) -> Type[CausalLMBatch]:
+        return CausalLMBatch
+
+    def decode(self, generated_ids: List[int]) -> str:
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=True, cleanup_tokenization_spaces=False
+        )
+
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
+        # Model Forward
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+        return outputs.logits, outputs.past_key_values
+
+    @tracer.start_as_current_span("generate_token")
+    def generate_token(
+        self, batch: CausalLMBatch
+    ) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
+        # slice the attention mask to the correct shape
+        attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
+
+        logits, past = self.forward(
+            batch.input_ids,
+            attention_mask,
+            batch.position_ids,
+            batch.past_key_values,
+        )
+
+        # Results
+        generations: List[Generation] = []
+        stopped = True
+
+        # Zipped iterator
+        iterator = zip(
+            batch.requests,
+            batch.input_lengths,
+            batch.offsets,
+            batch.token_offsets,
+            logits,
+            batch.next_token_choosers,
+            batch.stopping_criterias,
+            batch.all_input_ids,
+        )
+
+        # For each member of the batch
+        for i, (
+            request,
+            input_length,
+            offset,
+            token_offset,
+            logits,
+            next_token_chooser,
+            stopping_criteria,
+            all_input_ids,
+        ) in enumerate(iterator):
+            # Select next token
+            next_token_id, logprobs = next_token_chooser(
+                all_input_ids.view(1, -1), logits
+            )
+
+            # Append next token to all tokens
+            all_input_ids = torch.cat([all_input_ids, next_token_id])
+            new_input_length = input_length + 1
+
+            # Generated token
+            next_token_logprob = logprobs[-1, next_token_id]
+            next_token_id_squeezed = next_token_id.squeeze()
+            next_token_text, offset, token_offset = self.decode_token(
+                all_input_ids[:, 0], offset, token_offset
+            )
+
+            # Evaluate stopping criteria
+            stop, reason = stopping_criteria(
+                next_token_id_squeezed,
+                next_token_text,
+            )
+
+            if stop:
+                # Decode generated tokens
+                output_text = self.decode(
+                    all_input_ids[-stopping_criteria.current_tokens :, 0]
+                )
+                # Get seed
+                if isinstance(next_token_chooser.choice, Sampling):
+                    seed = next_token_chooser.choice.seed
+                else:
+                    seed = None
+
+                generated_text = GeneratedText(
+                    output_text, stopping_criteria.current_tokens, reason, seed
+                )
+            else:
+                # Keep request in the batch
+                generated_text = None
+                stopped = False
+
+            # Prefill
+            if stopping_criteria.current_tokens == 1:
+                # Remove generated token to only have prefill and add nan for first prompt token
+                prefill_logprobs = [float("nan")] + logprobs.gather(
+                    1, all_input_ids[1:]
+                ).squeeze(1)[-new_input_length:-1].tolist()
+                prefill_token_ids = all_input_ids[-new_input_length:-1]
+                prefill_texts = self.tokenizer.batch_decode(
+                    prefill_token_ids,
+                    clean_up_tokenization_spaces=False,
+                    skip_special_tokens=False,
+                )
+                prefill_tokens = PrefillTokens(
+                    prefill_token_ids, prefill_logprobs, prefill_texts
+                )
+            else:
+                prefill_tokens = None
+
+            generation = Generation(
+                request.id,
+                prefill_tokens,
+                next_token_id_squeezed,
+                next_token_logprob,
+                next_token_text,
+                next_token_id_squeezed.item() in self.all_special_ids,
+                generated_text,
+            )
+
+            generations.append(generation)
+
+            # Update values
+            batch.input_ids[i, 0] = next_token_id
+            batch.all_input_ids[i] = all_input_ids
+            batch.input_lengths[i] = new_input_length
+            batch.offsets[i] = offset
+            batch.token_offsets[i] = token_offset
+            batch.max_input_length = max(batch.max_input_length, new_input_length)
+
+        # We finished all generations in the batch; there is no next batch
+        if stopped:
+            return generations, None
+
+        # Slice unused values from prefill
+        batch.input_ids = batch.input_ids[:, :1]
+
+        # Update attention_mask as we added a new token to input_ids
+        batch.attention_mask[:, -batch.padding_right_offset] = 1
+        # Decrease right offset
+        batch.padding_right_offset -= 1
+
+        # Update position_ids
+        batch.position_ids = batch.position_ids[:, -1:] + 1
+
+        # Update past key values
+        batch.past_key_values = past
+
+        return generations, batch
diff --git a/server/text_generation_server/models/custom_modeling/__init__.py b/server/text_generation_server/models/custom_modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..de9b22daf9c9b824ab6150ee54430b9ef3f13fe9
--- /dev/null
+++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
@@ -0,0 +1,674 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.distributed
+
+from torch.nn import functional as F
+
+from torch import nn
+from transformers.activations import ACT2FN
+from typing import Optional
+
+# Flash attention imports
+import rotary_emb
+import flash_attn_cuda
+import dropout_layer_norm
+
+from flash_attn.layers.rotary import RotaryEmbedding
+
+HAS_BITS_AND_BYTES = True
+try:
+    from bitsandbytes.nn import Linear8bitLt
+except ImportError as e:
+    HAS_BITS_AND_BYTES = False
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states, residual=None):
+        if hidden_states.shape[-1] > 8192:
+            if residual is not None:
+                hidden_states += residual
+            residual = hidden_states
+
+            variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+            hidden_states = hidden_states * torch.rsqrt(
+                variance + self.variance_epsilon
+            )
+
+            # convert into half-precision if necessary
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                hidden_states = hidden_states.to(self.weight.dtype)
+
+            return self.weight * hidden_states, residual
+        else:
+            # faster post attention rms norm
+            normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
+                hidden_states,
+                residual,
+                self.weight,
+                None,
+                None,
+                None,
+                None,
+                None,
+                0.0,
+                self.variance_epsilon,
+                1.0,
+                0,
+                None,
+                False,
+                True,  # Activate RMSNorm
+            )
+            if res is None:
+                res = hidden_states
+
+            return normed_hidden_states, res
+
+
+class FastLinear(nn.Linear):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super(FastLinear, self).__init__(in_features, out_features, bias, device, dtype)
+        self.quantized = False
+        self.bnb_linear = None
+
+    def prepare_weights(self, quantize: bool = False):
+        if quantize:
+            if not HAS_BITS_AND_BYTES:
+                raise ImportError(
+                    "bitsandbytes is not available on your machine either because it is not installed "
+                    "or you don't have a GPU.\n"
+                    "You can install it with `pip install bitsandbytes`."
+                )
+
+            self.quantized = True
+            self.bnb_linear = Linear8bitLt(
+                self.in_features,
+                self.out_features,
+                has_fp16_weights=False,
+                threshold=6.0,
+                bias=False,
+            )
+            # Copy data to bnb_linear
+            self.bnb_linear.weight.data = self.weight.data
+            if self.bias is not None:
+                self.bnb_linear.bias = nn.Parameter(self.bias)
+
+            # Delete reference to data
+            self.weight = None
+            self.bias = None
+        else:
+            self.weight = nn.Parameter(self.weight.T)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        if self.quantized:
+            return self.bnb_linear(input)
+        else:
+            if self.bias is not None:
+                return torch.addmm(self.bias, input, self.weight)
+            return torch.matmul(input, self.weight)
+
+
+class TensorParallelColumnLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        assert out_features % self.tp_world_size == 0
+        out_features = out_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+
+class TensorParallelRowLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        reduce=True,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        self.reduce = reduce
+        assert in_features % self.tp_world_size == 0
+        in_features = in_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        out = super(TensorParallelRowLinear, self).forward(input)
+        if self.reduce:
+            torch.distributed.all_reduce(out, group=self.process_group)
+
+        return out
+
+
+class TensorParallelEmbedding(nn.Embedding):
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        process_group: torch.distributed.ProcessGroup,
+        padding_idx=None,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        sparse=False,
+        _weight=None,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_rank = process_group.rank()
+        self.tp_world_size = process_group.size()
+
+        self.original_num_embeddings = num_embeddings
+
+        assert num_embeddings % self.tp_world_size == 0
+        block_size = num_embeddings // self.tp_world_size
+        # inputs in `[min_id, max_id[` are handled by `self` to get embeddings
+        self.min_id = self.tp_rank * block_size
+        self.max_id = (self.tp_rank + 1) * block_size
+
+        # Additional entry that will map to zero
+        # Used for masking
+        self.null_idx = block_size
+
+        super().__init__(
+            block_size,
+            embedding_dim,
+            padding_idx=padding_idx,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            scale_grad_by_freq=scale_grad_by_freq,
+            sparse=sparse,
+            _weight=_weight,
+            device=device,
+            dtype=dtype,
+        )
+
+    def add_null_idx(self):
+        """Additional 0 entry used for masking"""
+        self.weight = nn.Parameter(F.pad(self.weight, (0, 0, 0, 1)))
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # default all out of bounds values to `self.null_idx` that will then be mapped to 0
+        # translate for [0, self.max_id - self.min_id[
+        input = torch.where(
+            (self.min_id > input) | (input >= self.max_id),
+            self.null_idx,
+            input - self.min_id,
+        )
+        out = super().forward(input)
+        torch.distributed.all_reduce(out, group=self.process_group)
+        return out
+
+
+class PositionRotaryEmbedding(RotaryEmbedding):
+    def _update_cos_sin_cache(self, dtype, device, seqlen):
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if (
+            seqlen > self._seq_len_cached
+            or self._cos_cached.device != device
+            or self._cos_cached.dtype != dtype
+        ):
+            self._seq_len_cached = seqlen
+            t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device=t.device))
+            self._cos_cached = torch.cos(freqs).to(dtype)
+            self._sin_cached = torch.sin(freqs).to(dtype)
+
+    def get_cos_sin(self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype):
+        """
+        Return cos and sin for the asked position ids
+        """
+
+        self._update_cos_sin_cache(dtype, position_ids.device, max_s)
+
+        cos = torch.index_select(self._cos_cached, 0, position_ids)
+        sin = torch.index_select(self._sin_cached, 0, position_ids)
+        return cos.unsqueeze(1), sin.unsqueeze(1)
+
+    def forward(self, qkv: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+        rotary_dim = cos.shape[-1]
+        q1 = qkv[:, 0, :, :rotary_dim]
+        q2 = qkv[:, 0, :, rotary_dim : 2 * rotary_dim]
+        k1 = qkv[:, 1, :, :rotary_dim]
+        k2 = qkv[:, 1, :, rotary_dim : 2 * rotary_dim]
+
+        rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return qkv
+
+
+class FlashLlamaAttention(torch.nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        hidden_size,
+        process_group=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.head_size = hidden_size // num_heads
+
+        self.rotary_emb = PositionRotaryEmbedding(self.head_size, base=10000)
+        self.softmax_scale = self.head_size ** (-0.5)
+
+        if process_group is None:
+            self.query_key_value = FastLinear(hidden_size, 3 * hidden_size, bias=False)
+            self.o_proj = FastLinear(hidden_size, hidden_size, bias=False)
+        else:
+            self.num_heads = self.num_heads // process_group.size()
+            self.query_key_value = TensorParallelColumnLinear(
+                hidden_size,
+                3 * hidden_size,
+                bias=False,
+                process_group=process_group,
+            )
+            self.o_proj = TensorParallelRowLinear(
+                hidden_size,
+                hidden_size,
+                bias=False,
+                process_group=process_group,
+            )
+
+    def forward(
+        self,
+        hidden_states,
+        cos,
+        sin,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        qkv = self.query_key_value(hidden_states)
+        qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
+        qkv_rot = self.rotary_emb(qkv, cos, sin)
+
+        # Prefill
+        if layer_past_present_indices is None:
+            # Copy to layer past
+            layer_past[...] = qkv_rot[:, 1:]
+
+            # output
+            attn_output = torch.empty_like(qkv_rot[:, 0])
+            # flash attention
+            flash_attn_cuda.fwd(
+                qkv_rot[:, 0],
+                qkv_rot[:, 1],
+                qkv_rot[:, 2],
+                attn_output,
+                cu_seqlens,
+                cu_seqlens,
+                max_s,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                True,
+                False,
+                0,
+                None,
+            )
+        # Decode
+        else:
+            query = qkv_rot[:, 0]
+            # Add present to the layer_past tensor at the correct indices
+            layer_past[layer_past_present_indices] = qkv_rot[:, 1:]
+
+            # output
+            attn_output = torch.empty_like(query)
+            # flash attention
+            flash_attn_cuda.fwd(
+                query,
+                layer_past[:, 0],
+                layer_past[:, 1],
+                attn_output,
+                cu_seqlens_q,
+                cu_seqlens,
+                1,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                False,
+                False,
+                0,
+                None,
+            )
+
+        return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
+
+
+class LlamaMLP(nn.Module):
+    def __init__(self, act, hidden_size, intermediate_size, process_group=None):
+        super().__init__()
+        self.act = (
+            ACT2FN[act]
+            if "gelu" not in act
+            else lambda x: torch.nn.functional.gelu(
+                x,
+                approximate="tanh"
+                if act in ["gelu_fast", "gelu_pytorch_tanh"]
+                else "none",
+            )
+        )
+
+        if process_group is None:
+            # Fuse gate and up proj
+            self.gate_up_proj = FastLinear(
+                hidden_size, 2 * intermediate_size, bias=False
+            )
+            self.down_proj = FastLinear(intermediate_size, hidden_size, bias=False)
+            self.intermediate_size = intermediate_size
+        else:
+            # Fuse gate and up proj
+            self.gate_up_proj = TensorParallelColumnLinear(
+                hidden_size,
+                2 * intermediate_size,
+                bias=False,
+                process_group=process_group,
+            )
+            self.down_proj = TensorParallelRowLinear(
+                intermediate_size,
+                hidden_size,
+                bias=False,
+                process_group=process_group,
+                reduce=True,
+            )
+            self.intermediate_size = self.down_proj.in_features
+
+        self.process_group = process_group
+
+    def forward(self, hidden_states):
+        gate_up_states = self.gate_up_proj(hidden_states)
+        gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
+        return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
+
+
+class FlashLlamaLayer(nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        act,
+        hidden_size,
+        intermediate_size,
+        rms_norm_eps,
+        process_group=None,
+    ):
+        super().__init__()
+
+        self.self_attn = FlashLlamaAttention(num_heads, hidden_size, process_group)
+        self.mlp = LlamaMLP(act, hidden_size, intermediate_size, process_group)
+
+        self.input_layernorm = LlamaRMSNorm(hidden_size, eps=rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(hidden_size, eps=rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
+
+        # Self Attention
+        attn_output = self.self_attn(
+            normed_hidden_states,
+            cos,
+            sin,
+            cu_seqlens,
+            max_s,
+            layer_past,
+            layer_past_present_indices,
+            cu_seqlens_q,
+        )
+
+        # faster post attention rms norm
+        normed_attn_res_output, attn_res = self.post_attention_layernorm(
+            attn_output, res
+        )
+
+        mlp_output = self.mlp(normed_attn_res_output)
+
+        return mlp_output, attn_res
+
+
+class FlashLlamaModel(torch.nn.Module):
+    def __init__(self, config, process_group=None):
+        super(FlashLlamaModel, self).__init__()
+        self.config = config
+
+        self.tp_embeddings = False
+        if process_group is not None:
+            self.tp_rank = process_group.rank()
+            self.tp_world_size = process_group.size()
+            if config.vocab_size % self.tp_world_size == 0:
+                self.tp_embeddings = True
+
+        if self.tp_embeddings:
+            self.embed_tokens = TensorParallelEmbedding(
+                config.vocab_size, config.hidden_size, process_group=process_group
+            )
+        else:
+            self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+
+        self.layers = nn.ModuleList(
+            [
+                FlashLlamaLayer(
+                    config.num_attention_heads,
+                    config.hidden_act,
+                    config.hidden_size,
+                    config.intermediate_size,
+                    config.rms_norm_eps,
+                    process_group,
+                )
+                for _ in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+
+        self.head_size = self.layers[0].self_attn.head_size
+        self.num_heads = self.layers[0].self_attn.num_heads
+
+    def post_load_weights(self, load_in_8bit: bool = False):
+        if isinstance(self.embed_tokens, TensorParallelEmbedding):
+            self.embed_tokens.add_null_idx()
+        for layer in self.layers:
+            layer: FlashLlamaLayer
+            layer.self_attn.query_key_value.prepare_weights(load_in_8bit)
+            layer.self_attn.o_proj.prepare_weights(load_in_8bit)
+            layer.mlp.gate_up_proj.prepare_weights(load_in_8bit)
+            layer.mlp.down_proj.prepare_weights(load_in_8bit)
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values: Optional[torch.Tensor] = None,
+        pre_allocate_past_size: Optional[int] = None,
+    ):
+        hidden_states = self.embed_tokens(input_ids)
+
+        # Prefill
+        if past_key_values is None:
+            # Create past tensor
+            past_key_values = hidden_states.new_empty(
+                (
+                    len(self.layers),
+                    len(hidden_states)
+                    if pre_allocate_past_size is None
+                    else pre_allocate_past_size,
+                    2,
+                    self.num_heads,
+                    self.head_size,
+                )
+            )
+            layer_past_present_indices = None
+            cu_seqlens_q = None
+            slice_past_index = len(hidden_states)
+        # Decode
+        else:
+            # Create indices from cumulative sequence lengths
+            layer_past_present_indices = cu_seqlens[1:] - 1
+            cu_seqlens_q = torch.arange(
+                cu_seqlens.shape[0], dtype=torch.int32, device=hidden_states.device
+            )
+            slice_past_index = None
+
+        # Get rotary cos and sin for this forward
+        # Avoid to index in each layer
+        cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
+            position_ids, max_s, hidden_states.dtype
+        )
+
+        residual = None
+        for i, layer in enumerate(self.layers):
+            # We added padding that we now need to slice
+            layer_past_key_values = (
+                past_key_values[i]
+                if slice_past_index is None
+                else past_key_values[i, :slice_past_index]
+            )
+
+            hidden_states, residual = layer(
+                hidden_states,
+                residual,
+                cos,
+                sin,
+                cu_seqlens,
+                max_s,
+                layer_past_key_values,
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        return hidden_states, past_key_values
+
+
+class FlashLlamaForCausalLM(torch.nn.Module):
+    def __init__(self, config, process_group=None):
+        super().__init__()
+
+        self.process_group = process_group
+        if self.process_group is not None:
+            self.world_size = self.process_group.size()
+            self.rank = self.process_group.rank()
+        else:
+            self.world_size = 1
+            self.rank = 0
+
+        self.model = FlashLlamaModel(config, process_group)
+
+        if self.model.tp_embeddings:
+            self.lm_head = FastLinear(
+                config.hidden_size,
+                config.vocab_size // process_group.size(),
+                bias=False,
+            )
+        else:
+            self.lm_head = FastLinear(config.hidden_size, config.vocab_size, bias=False)
+
+    def post_load_weights(self, load_in_8bit: bool = False):
+        self.model.post_load_weights(load_in_8bit)
+        self.lm_head.prepare_weights()
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values: Optional[torch.Tensor] = None,
+        pre_allocate_past_size: Optional[int] = None,
+    ):
+        hidden_states, present = self.model(
+            input_ids,
+            position_ids,
+            cu_seqlens,
+            max_s,
+            past_key_values,
+            pre_allocate_past_size,
+        )
+        logits = self.lm_head(hidden_states)
+
+        if self.model.tp_embeddings:
+            # Logits are sharded, so we need to gather them
+            world_logits = [torch.empty_like(logits) for _ in range(self.world_size)]
+            torch.distributed.all_gather(world_logits, logits, group=self.process_group)
+            world_logits = torch.cat(world_logits, dim=1)
+
+            return world_logits, present
+        return logits, present
diff --git a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc9b292f8f3364c98eab0625450e44f89fa878ee
--- /dev/null
+++ b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
@@ -0,0 +1,750 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.distributed
+
+from torch.nn import functional as F
+
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.gpt_neox import GPTNeoXConfig
+from typing import Optional
+
+# Flash attention imports
+import rotary_emb
+import flash_attn_cuda
+import dropout_layer_norm
+
+from flash_attn.layers.rotary import RotaryEmbedding
+
+HAS_BITS_AND_BYTES = True
+try:
+    from bitsandbytes.nn import Linear8bitLt
+except ImportError as e:
+    HAS_BITS_AND_BYTES = False
+
+
+class FastLayerNorm(nn.LayerNorm):
+    def forward(self, hidden_states, residual=None):
+        if hidden_states.shape[-1] > 8192:
+            if residual is not None:
+                hidden_states += residual
+            residual = hidden_states
+
+            return super(FastLayerNorm, self).forward(hidden_states), residual
+        else:
+            (
+                normed_hidden_states,
+                residual,
+                *rest,
+            ) = dropout_layer_norm.dropout_add_ln_fwd(
+                hidden_states,
+                residual,
+                self.weight,
+                self.bias,
+                None,
+                None,
+                None,
+                None,
+                0.0,
+                self.eps,
+                1.0,
+                0,
+                None,
+                False,
+                False,
+            )
+            if residual is None:
+                residual = hidden_states
+
+            return normed_hidden_states, residual
+
+
+class FastLinear(nn.Linear):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super(FastLinear, self).__init__(in_features, out_features, bias, device, dtype)
+        self.quantized = False
+        self.bnb_linear = None
+
+    def prepare_weights(self, quantize: bool = False):
+        if quantize:
+            if not HAS_BITS_AND_BYTES:
+                raise ImportError(
+                    "bitsandbytes is not available on your machine either because it is not installed "
+                    "or you don't have a GPU.\n"
+                    "You can install it with `pip install bitsandbytes`."
+                )
+
+            self.quantized = True
+            self.bnb_linear = Linear8bitLt(
+                self.in_features,
+                self.out_features,
+                has_fp16_weights=False,
+                threshold=6.0,
+                bias=False,
+            )
+            # Copy data to bnb_linear
+            self.bnb_linear.weight.data = self.weight.data
+            if self.bias is not None:
+                self.bnb_linear.bias = nn.Parameter(self.bias)
+
+            # Delete reference to data
+            self.weight = None
+            self.bias = None
+        else:
+            self.weight = nn.Parameter(self.weight.T)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        if self.quantized:
+            return self.bnb_linear(input)
+        else:
+            if self.bias is not None:
+                return torch.addmm(self.bias, input, self.weight)
+            return torch.matmul(input, self.weight)
+
+
+class TensorParallelColumnLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        assert out_features % self.tp_world_size == 0
+        out_features = out_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+
+class TensorParallelRowLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        reduce=True,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        self.reduce = reduce
+        assert in_features % self.tp_world_size == 0
+        in_features = in_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        out = super(TensorParallelRowLinear, self).forward(input)
+        if self.reduce:
+            torch.distributed.all_reduce(out, group=self.process_group)
+
+        return out
+
+
+class TensorParallelEmbedding(nn.Embedding):
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        process_group: torch.distributed.ProcessGroup,
+        padding_idx=None,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        sparse=False,
+        _weight=None,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_rank = process_group.rank()
+        self.tp_world_size = process_group.size()
+
+        self.original_num_embeddings = num_embeddings
+
+        assert num_embeddings % self.tp_world_size == 0
+        block_size = num_embeddings // self.tp_world_size
+        # inputs in `[min_id, max_id[` are handled by `self` to get embeddings
+        self.min_id = self.tp_rank * block_size
+        self.max_id = (self.tp_rank + 1) * block_size
+
+        # Additional entry that will map to zero
+        # Used for masking
+        self.null_idx = block_size
+
+        super().__init__(
+            block_size,
+            embedding_dim,
+            padding_idx=padding_idx,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            scale_grad_by_freq=scale_grad_by_freq,
+            sparse=sparse,
+            _weight=_weight,
+            device=device,
+            dtype=dtype,
+        )
+
+    def add_null_idx(self):
+        """Additional 0 entry used for masking"""
+        self.weight = nn.Parameter(F.pad(self.weight, (0, 0, 0, 1)))
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # default all out of bounds values to `self.null_idx` that will then be mapped to 0
+        # translate for [0, self.max_id - self.min_id[
+        input = torch.where(
+            (self.min_id > input) | (input >= self.max_id),
+            self.null_idx,
+            input - self.min_id,
+        )
+        out = super().forward(input)
+        torch.distributed.all_reduce(out, group=self.process_group)
+        return out
+
+
+class PositionRotaryEmbedding(RotaryEmbedding):
+    def _update_cos_sin_cache(self, dtype, device, seqlen):
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if (
+            seqlen > self._seq_len_cached
+            or self._cos_cached.device != device
+            or self._cos_cached.dtype != dtype
+        ):
+            self._seq_len_cached = seqlen
+            t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
+            # Don't do einsum, it converts fp32 to fp16
+            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            freqs = torch.outer(t, self.inv_freq.to(device=t.device))
+            self._cos_cached = torch.cos(freqs).to(dtype)
+            self._sin_cached = torch.sin(freqs).to(dtype)
+
+    def get_cos_sin(self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype):
+        """
+        Return cos and sin for the asked position ids
+        """
+
+        self._update_cos_sin_cache(dtype, position_ids.device, max_s)
+
+        cos = torch.index_select(self._cos_cached, 0, position_ids)
+        sin = torch.index_select(self._sin_cached, 0, position_ids)
+        return cos.unsqueeze(1), sin.unsqueeze(1)
+
+    def forward(self, qkv: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+        rotary_dim = cos.shape[-1]
+        q1 = qkv[:, 0, :, :rotary_dim]
+        q2 = qkv[:, 0, :, rotary_dim : 2 * rotary_dim]
+        k1 = qkv[:, 1, :, :rotary_dim]
+        k2 = qkv[:, 1, :, rotary_dim : 2 * rotary_dim]
+
+        rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return qkv
+
+
+class FlashNeoxAttention(torch.nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        hidden_size,
+        rotary_pct,
+        rotary_emb_base,
+        process_group=None,
+        reduce=True,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.head_size = hidden_size // num_heads
+
+        rotary_ndims = int(self.head_size * rotary_pct)
+        self.rotary_emb = PositionRotaryEmbedding(rotary_ndims, base=rotary_emb_base)
+        self.softmax_scale = self.head_size ** (-0.5)
+
+        if process_group is None:
+            self.query_key_value = FastLinear(hidden_size, 3 * hidden_size)
+            self.dense = FastLinear(hidden_size, hidden_size)
+        else:
+            self.num_heads = self.num_heads // process_group.size()
+            self.query_key_value = TensorParallelColumnLinear(
+                hidden_size,
+                3 * hidden_size,
+                process_group=process_group,
+            )
+            self.dense = TensorParallelRowLinear(
+                hidden_size, hidden_size, process_group=process_group, reduce=reduce
+            )
+
+    def shuffle_qkv_dims(self):
+        """Swap dims to avoid an additional permute"""
+        self.query_key_value.weight = torch.nn.Parameter(
+            self.query_key_value.weight.view(
+                self.num_heads, 3, self.head_size, self.hidden_size
+            )
+            .permute(1, 0, 2, 3)
+            .reshape(-1, self.hidden_size)
+        )
+        self.query_key_value.bias = torch.nn.Parameter(
+            self.query_key_value.bias.view(self.num_heads, 3, self.head_size)
+            .permute(1, 0, 2)
+            .reshape(-1)
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        cos,
+        sin,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        qkv = self.query_key_value(hidden_states)
+        qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
+        qkv_rot = self.rotary_emb(qkv, cos, sin)
+
+        # Prefill
+        if layer_past_present_indices is None:
+            # Copy to layer past
+            layer_past[...] = qkv_rot[:, 1:]
+
+            # output
+            attn_output = torch.empty_like(qkv_rot[:, 0])
+            # flash attention
+            flash_attn_cuda.fwd(
+                qkv_rot[:, 0],
+                qkv_rot[:, 1],
+                qkv_rot[:, 2],
+                attn_output,
+                cu_seqlens,
+                cu_seqlens,
+                max_s,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                True,
+                False,
+                0,
+                None,
+            )
+        # Decode
+        else:
+            query = qkv_rot[:, 0]
+            # Add present to the layer_past tensor at the correct indices
+            layer_past[layer_past_present_indices] = qkv_rot[:, 1:]
+
+            # output
+            attn_output = torch.empty_like(query)
+            # flash attention
+            flash_attn_cuda.fwd(
+                query,
+                layer_past[:, 0],
+                layer_past[:, 1],
+                attn_output,
+                cu_seqlens_q,
+                cu_seqlens,
+                1,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                False,
+                False,
+                0,
+                None,
+            )
+
+        return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
+
+
+class FlashMLP(nn.Module):
+    def __init__(
+        self, act, hidden_size, intermediate_size, process_group=None, reduce=True
+    ):
+        super().__init__()
+        self.act = (
+            ACT2FN[act]
+            if "gelu" not in act
+            else lambda x: torch.nn.functional.gelu(
+                x,
+                approximate="tanh"
+                if act in ["gelu_fast", "gelu_pytorch_tanh"]
+                else "none",
+            )
+        )
+
+        if process_group is None:
+            self.dense_h_to_4h = FastLinear(hidden_size, intermediate_size)
+            self.dense_4h_to_h = FastLinear(intermediate_size, hidden_size)
+        else:
+            self.dense_h_to_4h = TensorParallelColumnLinear(
+                hidden_size,
+                intermediate_size,
+                process_group=process_group,
+            )
+            self.dense_4h_to_h = TensorParallelRowLinear(
+                intermediate_size,
+                hidden_size,
+                process_group=process_group,
+                reduce=reduce,
+            )
+        self.process_group = process_group
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_h_to_4h(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.dense_4h_to_h(hidden_states)
+        return hidden_states
+
+
+class FlashNeoXLayer(nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        act,
+        hidden_size,
+        intermediate_size,
+        rotary_pct,
+        rotary_emb_base,
+        layer_norm_eps,
+        use_parallel_residual,
+        process_group=None,
+    ):
+        super().__init__()
+        self.use_parallel_residual = use_parallel_residual
+        self.input_layernorm = FastLayerNorm(hidden_size, eps=layer_norm_eps)
+        self.post_attention_layernorm = FastLayerNorm(hidden_size, eps=layer_norm_eps)
+        self.attention = FlashNeoxAttention(
+            num_heads,
+            hidden_size,
+            rotary_pct,
+            rotary_emb_base,
+            process_group,
+            reduce=not use_parallel_residual,
+        )
+        self.mlp = FlashMLP(
+            act,
+            hidden_size,
+            intermediate_size,
+            process_group,
+            reduce=not use_parallel_residual,
+        )
+        self.process_group = process_group
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        if self.use_parallel_residual:
+            ln1_hidden_states, _ = self.input_layernorm(hidden_states)
+
+            attn_output = self.attention(
+                ln1_hidden_states,
+                cos,
+                sin,
+                cu_seqlens,
+                max_s,
+                layer_past,
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+            ln2_hidden_states, _ = self.post_attention_layernorm(hidden_states)
+
+            mlp_output = self.mlp(ln2_hidden_states)
+            intermediate = mlp_output + attn_output
+
+            # Only reduce once and after the addition instead of once per layer
+            if self.process_group is not None:
+                torch.distributed.all_reduce(intermediate, group=self.process_group)
+
+            return intermediate + hidden_states, None
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+            hidden_states = self.attention(
+                hidden_states,
+                cos,
+                sin,
+                cu_seqlens,
+                max_s,
+                layer_past,
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+            hidden_states, residual = self.post_attention_layernorm(
+                hidden_states, residual
+            )
+
+            mlp_output = self.mlp(hidden_states)
+
+            return mlp_output, residual
+
+
+class FlashGPTNeoXPreTrainedModel(PreTrainedModel):
+    config_class = GPTNeoXConfig
+    base_model_prefix = "gpt_neox"
+    supports_gradient_checkpointing = False
+    _no_split_modules = None
+
+
+class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
+    def __init__(self, config, process_group=None):
+        super().__init__(config)
+        self.config = config
+
+        self.tp_embeddings = False
+        if process_group is not None:
+            self.tp_rank = process_group.rank()
+            self.tp_world_size = process_group.size()
+            if config.vocab_size % self.tp_world_size == 0:
+                self.tp_embeddings = True
+
+        if self.tp_embeddings:
+            self.embed_in = TensorParallelEmbedding(
+                config.vocab_size, config.hidden_size, process_group=process_group
+            )
+        else:
+            self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
+
+        self.layers = nn.ModuleList(
+            [
+                FlashNeoXLayer(
+                    config.num_attention_heads,
+                    config.hidden_act,
+                    config.hidden_size,
+                    config.intermediate_size,
+                    config.rotary_pct,
+                    config.rotary_emb_base,
+                    config.layer_norm_eps,
+                    config.use_parallel_residual,
+                    process_group,
+                )
+                for _ in range(config.num_hidden_layers)
+            ]
+        )
+        self.final_layer_norm = FastLayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+
+        self.gradient_checkpointing = False
+
+        self.head_size = self.layers[0].attention.head_size
+        self.num_heads = self.layers[0].attention.num_heads
+
+    def post_load_weights(self, load_in_8bit=False):
+        if isinstance(self.embed_in, TensorParallelEmbedding):
+            self.embed_in.add_null_idx()
+        for layer in self.layers:
+            layer: FlashNeoXLayer
+            layer.attention.shuffle_qkv_dims()
+            layer.attention.query_key_value.prepare_weights(load_in_8bit)
+            layer.attention.dense.prepare_weights(load_in_8bit)
+            layer.mlp.dense_h_to_4h.prepare_weights(load_in_8bit)
+            layer.mlp.dense_4h_to_h.prepare_weights(load_in_8bit)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        # Pop here as we will replace the layer in our own logic and don't want from_pretrained
+        # to do it for us
+        load_in_8bit = kwargs.pop("load_in_8bit", False)
+        model = super(FlashGPTNeoXModel, cls).from_pretrained(
+            pretrained_model_name_or_path, load_in_8bit=False, *model_args, **kwargs
+        )
+
+        model.post_load_weights(load_in_8bit)
+        return model
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values=None,
+        pre_allocate_past_size: Optional[int] = None,
+    ):
+        hidden_states = self.embed_in(input_ids)
+
+        # Prefill
+        if past_key_values is None:
+            # Create past tensor
+            past_key_values = hidden_states.new_empty(
+                (
+                    len(self.layers),
+                    len(hidden_states)
+                    if pre_allocate_past_size is None
+                    else pre_allocate_past_size,
+                    2,
+                    self.num_heads,
+                    self.head_size,
+                )
+            )
+            layer_past_present_indices = None
+            cu_seqlens_q = None
+            slice_past_index = len(hidden_states)
+        # Decode
+        else:
+            # Create indices from cumulative sequence lengths
+            layer_past_present_indices = cu_seqlens[1:] - 1
+            cu_seqlens_q = torch.arange(
+                cu_seqlens.shape[0], dtype=torch.int32, device=hidden_states.device
+            )
+            slice_past_index = None
+
+        # Get rotary cos and sin for this forward
+        # Avoid to index in each layer
+        cos, sin = self.layers[0].attention.rotary_emb.get_cos_sin(
+            position_ids, max_s, hidden_states.dtype
+        )
+
+        residual = None
+        for i, layer in enumerate(self.layers):
+            # We added padding that we now need to slice
+            layer_past_key_values = (
+                past_key_values[i]
+                if slice_past_index is None
+                else past_key_values[i, :slice_past_index]
+            )
+
+            hidden_states, residual = layer(
+                hidden_states,
+                residual,
+                cos,
+                sin,
+                cu_seqlens,
+                max_s,
+                layer_past_key_values,
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+        hidden_states, _ = self.final_layer_norm(hidden_states, residual)
+
+        return hidden_states, past_key_values
+
+
+class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
+    def __init__(self, config, process_group=None):
+        super().__init__(config)
+
+        self.process_group = process_group
+        if self.process_group is not None:
+            self.world_size = self.process_group.size()
+            self.rank = self.process_group.rank()
+        else:
+            self.world_size = 1
+            self.rank = 0
+
+        self.gpt_neox = FlashGPTNeoXModel(config, process_group)
+
+        if self.gpt_neox.tp_embeddings:
+            self.embed_out = FastLinear(
+                config.hidden_size,
+                config.vocab_size // process_group.size(),
+                bias=False,
+            )
+        else:
+            self.embed_out = FastLinear(
+                config.hidden_size, config.vocab_size, bias=False
+            )
+
+    def post_load_weights(self, load_in_8bit=False):
+        self.gpt_neox.post_load_weights(load_in_8bit)
+        self.embed_out.prepare_weights()
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        # Pop here as we will replace the layer in our own logic and don't want from_pretrained
+        # to do it for us
+        load_in_8bit = kwargs.pop("load_in_8bit", False)
+        model = super(FlashGPTNeoXForCausalLM, cls).from_pretrained(
+            pretrained_model_name_or_path, load_in_8bit=False, *model_args, **kwargs
+        )
+        model.post_load_weights(load_in_8bit)
+        return model
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values: Optional[torch.Tensor] = None,
+        pre_allocate_past_size: Optional[int] = None,
+    ):
+        hidden_states, present = self.gpt_neox(
+            input_ids,
+            position_ids,
+            cu_seqlens,
+            max_s,
+            past_key_values,
+            pre_allocate_past_size,
+        )
+        logits = self.embed_out(hidden_states)
+
+        if self.gpt_neox.tp_embeddings:
+            # Logits are sharded, so we need to gather them
+            world_logits = [torch.empty_like(logits) for _ in range(self.world_size)]
+            torch.distributed.all_gather(world_logits, logits, group=self.process_group)
+            world_logits = torch.cat(world_logits, dim=1)
+
+            return world_logits, present
+        return logits, present
diff --git a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..71182f8d44809b0a223ccde816f62ed4a77b1b6e
--- /dev/null
+++ b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
@@ -0,0 +1,595 @@
+import torch
+import torch.distributed
+
+import torch.nn.functional as F
+
+from torch import nn
+from transformers.activations import ACT2FN
+from typing import Optional
+
+# Flash attention imports
+import flash_attn_cuda
+import dropout_layer_norm
+
+HAS_BITS_AND_BYTES = True
+try:
+    from bitsandbytes.nn import Linear8bitLt
+except ImportError as e:
+    HAS_BITS_AND_BYTES = False
+
+
+class FastLayerNorm(nn.LayerNorm):
+    def forward(self, hidden_states, residual=None):
+        if hidden_states.shape[-1] > 8192:
+            if residual is not None:
+                hidden_states += residual
+            residual = hidden_states
+
+            return super(FastLayerNorm, self).forward(hidden_states), residual
+        else:
+            (
+                normed_hidden_states,
+                residual,
+                *rest,
+            ) = dropout_layer_norm.dropout_add_ln_fwd(
+                hidden_states,
+                residual,
+                self.weight,
+                self.bias,
+                None,
+                None,
+                None,
+                None,
+                0.0,
+                self.eps,
+                1.0,
+                0,
+                None,
+                False,
+                False,
+            )
+            if residual is None:
+                residual = hidden_states
+
+            return normed_hidden_states, residual
+
+
+class FastLinear(nn.Linear):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super(FastLinear, self).__init__(in_features, out_features, bias, device, dtype)
+        self.quantized = False
+        self.bnb_linear = None
+
+    def prepare_weights(self, quantize: bool = False):
+        if quantize:
+            if not HAS_BITS_AND_BYTES:
+                raise ImportError(
+                    "bitsandbytes is not available on your machine either because it is not installed "
+                    "or you don't have a GPU.\n"
+                    "You can install it with `pip install bitsandbytes`."
+                )
+
+            self.quantized = True
+            self.bnb_linear = Linear8bitLt(
+                self.in_features,
+                self.out_features,
+                has_fp16_weights=False,
+                threshold=6.0,
+                bias=False,
+            )
+            # Copy data to bnb_linear
+            self.bnb_linear.weight.data = self.weight.data
+            if self.bias is not None:
+                self.bnb_linear.bias = nn.Parameter(self.bias)
+
+            # Delete reference to data
+            self.weight = None
+            self.bias = None
+        else:
+            self.weight = nn.Parameter(self.weight.T)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        if self.quantized:
+            return self.bnb_linear(input)
+        else:
+            if self.bias is not None:
+                return torch.addmm(self.bias, input, self.weight)
+            return torch.matmul(input, self.weight)
+
+
+class TensorParallelColumnLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        assert out_features % self.tp_world_size == 0
+        out_features = out_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+
+class TensorParallelRowLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        reduce=True,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        self.reduce = reduce
+        assert in_features % self.tp_world_size == 0
+        in_features = in_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        out = super(TensorParallelRowLinear, self).forward(input)
+        if self.reduce:
+            torch.distributed.all_reduce(out, group=self.process_group)
+
+        return out
+
+
+class TensorParallelEmbedding(nn.Embedding):
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        process_group: torch.distributed.ProcessGroup,
+        reduce=True,
+        padding_idx=None,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        sparse=False,
+        _weight=None,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_rank = process_group.rank()
+        self.tp_world_size = process_group.size()
+        self.reduce = reduce
+
+        self.original_num_embeddings = num_embeddings
+
+        assert num_embeddings % self.tp_world_size == 0
+        block_size = num_embeddings // self.tp_world_size
+        # inputs in `[min_id, max_id[` are handled by `self` to get embeddings
+        self.min_id = self.tp_rank * block_size
+        self.max_id = (self.tp_rank + 1) * block_size
+
+        # Additional entry that will map to zero
+        # Used for masking
+        self.null_idx = block_size
+
+        super().__init__(
+            block_size,
+            embedding_dim,
+            padding_idx=padding_idx,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            scale_grad_by_freq=scale_grad_by_freq,
+            sparse=sparse,
+            _weight=_weight,
+            device=device,
+            dtype=dtype,
+        )
+
+    def add_null_idx(self):
+        """Additional 0 entry used for masking"""
+        self.weight = nn.Parameter(F.pad(self.weight, (0, 0, 0, 1)))
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # default all out of bounds values to `self.null_idx` that will then be mapped to 0
+        # translate for [0, self.max_id - self.min_id[
+        input = torch.where(
+            (self.min_id > input) | (input >= self.max_id),
+            self.null_idx,
+            input - self.min_id,
+        )
+        out = super().forward(input)
+        if self.reduce:
+            torch.distributed.all_reduce(out, group=self.process_group)
+        return out
+
+
+class FlashMQAttention(torch.nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        hidden_size,
+        process_group=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.head_size = hidden_size // num_heads
+
+        self.softmax_scale = self.head_size ** (-0.5)
+
+        if process_group is None:
+            self.c_attn = FastLinear(hidden_size, hidden_size + 2 * self.head_size)
+            self.c_proj = FastLinear(hidden_size, hidden_size)
+        else:
+            self.num_heads = self.num_heads // process_group.size()
+            self.c_attn = FastLinear(hidden_size, self.head_size * (self.num_heads + 2))
+            self.c_proj = TensorParallelRowLinear(
+                hidden_size,
+                hidden_size,
+                process_group=process_group,
+            )
+
+    def forward(
+        self,
+        hidden_states,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        qkv = self.c_attn(hidden_states)
+
+        # Split query from key_value
+        query, key_value = qkv.split(
+            [self.head_size * self.num_heads, 2 * self.head_size], dim=1
+        )
+
+        # Prepare query and key_value for indexing
+        query = query.view(-1, self.num_heads, self.head_size)
+        key_value = key_value.view(-1, 2, 1, self.head_size)
+
+        # Prefill
+        if layer_past_present_indices is None:
+            # Copy to layer past
+            layer_past[...] = key_value
+            # Expand from 1 to num_heads
+            key_value = key_value.expand(-1, 2, self.num_heads, self.head_size)
+
+            # output
+            attn_output = torch.empty_like(query)
+            # flash attention
+            flash_attn_cuda.fwd(
+                query,
+                key_value[:, 0],
+                key_value[:, 1],
+                attn_output,
+                cu_seqlens,
+                cu_seqlens,
+                max_s,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                True,
+                False,
+                0,
+                None,
+            )
+        # Decode
+        else:
+            # Add present to the layer_past tensor at the correct indices
+            layer_past[layer_past_present_indices] = key_value
+            # Expand from 1 to num_heads
+            key_value = layer_past.expand(-1, 2, self.num_heads, self.head_size)
+
+            # output
+            attn_output = torch.empty_like(query)
+            # flash attention
+            flash_attn_cuda.fwd(
+                query,
+                key_value[:, 0],
+                key_value[:, 1],
+                attn_output,
+                cu_seqlens_q,
+                cu_seqlens,
+                1,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                False,
+                False,
+                0,
+                None,
+            )
+
+        return self.c_proj(attn_output.view(-1, self.num_heads * self.head_size))
+
+
+class MLP(nn.Module):
+    def __init__(self, act, hidden_size, intermediate_size, process_group=None):
+        super().__init__()
+        self.act = (
+            ACT2FN[act]
+            if "gelu" not in act
+            else lambda x: torch.nn.functional.gelu(
+                x,
+                approximate="tanh"
+                if act in ["gelu_fast", "gelu_pytorch_tanh"]
+                else "none",
+            )
+        )
+
+        if process_group is None:
+            self.c_fc = FastLinear(hidden_size, intermediate_size)
+            self.c_proj = FastLinear(intermediate_size, hidden_size)
+        else:
+            self.c_fc = TensorParallelColumnLinear(
+                hidden_size,
+                intermediate_size,
+                process_group=process_group,
+            )
+            self.c_proj = TensorParallelRowLinear(
+                intermediate_size,
+                hidden_size,
+                process_group=process_group,
+            )
+
+    def forward(self, hidden_states):
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        act,
+        hidden_size,
+        intermediate_size,
+        layer_norm_eps,
+        process_group=None,
+    ):
+        super().__init__()
+        self.ln_1 = FastLayerNorm(hidden_size, eps=layer_norm_eps)
+        self.ln_2 = FastLayerNorm(hidden_size, eps=layer_norm_eps)
+        self.attn = FlashMQAttention(
+            num_heads,
+            hidden_size,
+            process_group,
+        )
+        self.mlp = MLP(
+            act,
+            hidden_size,
+            intermediate_size,
+            process_group,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        hidden_states, residual = self.ln_1(hidden_states, residual)
+
+        hidden_states = self.attn(
+            hidden_states,
+            cu_seqlens,
+            max_s,
+            layer_past,
+            layer_past_present_indices,
+            cu_seqlens_q,
+        )
+
+        hidden_states, residual = self.ln_2(hidden_states, residual)
+
+        mlp_output = self.mlp(hidden_states)
+
+        return mlp_output, residual
+
+
+class FlashSantacoderModel(nn.Module):
+    def __init__(self, config, process_group=None):
+        super().__init__()
+        self.config = config
+
+        self.process_group = process_group
+        self.tp_embeddings = False
+        if process_group is not None:
+            self.tp_rank = process_group.rank()
+            self.tp_world_size = process_group.size()
+            if config.vocab_size % self.tp_world_size == 0:
+                self.tp_embeddings = True
+
+        if self.tp_embeddings:
+            self.wte = TensorParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                reduce=False,
+                process_group=process_group,
+            )
+            self.wpe = TensorParallelEmbedding(
+                config.max_position_embeddings,
+                config.hidden_size,
+                reduce=False,
+                process_group=process_group,
+            )
+        else:
+            self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
+            self.wpe = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+        self.h = nn.ModuleList(
+            [
+                Block(
+                    config.num_attention_heads,
+                    config.activation_function,
+                    config.hidden_size,
+                    config.n_inner
+                    if config.n_inner is not None
+                    else 4 * config.hidden_size,
+                    config.layer_norm_epsilon,
+                    process_group,
+                )
+                for _ in range(config.num_hidden_layers)
+            ]
+        )
+        self.ln_f = FastLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        self.head_size = self.h[0].attn.head_size
+        self.num_heads = self.h[0].attn.num_heads
+
+    def post_load_weights(self, load_in_8bit: bool = False):
+        if self.tp_embeddings:
+            self.wte.add_null_idx()
+            self.wpe.add_null_idx()
+        for layer in self.h:
+            layer: Block
+            layer.attn.c_attn.prepare_weights(load_in_8bit)
+            layer.attn.c_proj.prepare_weights(load_in_8bit)
+            layer.mlp.c_fc.prepare_weights(load_in_8bit)
+            layer.mlp.c_proj.prepare_weights(load_in_8bit)
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values: Optional[torch.Tensor] = None,
+        pre_allocate_past_size: Optional[int] = None,
+    ):
+        hidden_states = self.wte(input_ids) + self.wpe(position_ids)
+        if self.tp_embeddings:
+            torch.distributed.all_reduce(hidden_states, group=self.process_group)
+
+        # Prefill
+        if past_key_values is None:
+            # Create past tensor
+            past_key_values = hidden_states.new_empty(
+                (
+                    len(self.h),
+                    len(hidden_states)
+                    if pre_allocate_past_size is None
+                    else pre_allocate_past_size,
+                    2,
+                    1,
+                    self.head_size,
+                )
+            )
+            layer_past_present_indices = None
+            cu_seqlens_q = None
+            slice_past_index = len(hidden_states)
+        # Decode
+        else:
+            # Create indices from cumulative sequence lengths
+            layer_past_present_indices = cu_seqlens[1:] - 1
+            cu_seqlens_q = torch.arange(
+                cu_seqlens.shape[0], dtype=torch.int32, device=hidden_states.device
+            )
+            slice_past_index = None
+
+        residual = None
+        for i, layer in enumerate(self.h):
+            # We added padding that we now need to slice
+            layer_past_key_values = (
+                past_key_values[i]
+                if slice_past_index is None
+                else past_key_values[i, :slice_past_index]
+            )
+
+            hidden_states, residual = layer(
+                hidden_states,
+                residual,
+                cu_seqlens,
+                max_s,
+                layer_past_key_values,
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+        hidden_states, _ = self.ln_f(hidden_states, residual)
+
+        return hidden_states, past_key_values
+
+
+class FlashSantacoderForCausalLM(nn.Module):
+    def __init__(self, config, process_group=None):
+        super().__init__()
+
+        self.transformer = FlashSantacoderModel(config, process_group)
+
+        if self.transformer.tp_embeddings:
+            self.lm_head = FastLinear(
+                config.hidden_size,
+                config.vocab_size // process_group.size(),
+                bias=False,
+            )
+        else:
+            self.lm_head = FastLinear(config.hidden_size, config.vocab_size, bias=False)
+
+    def post_load_weights(self, load_in_8bit: bool = False):
+        self.transformer.post_load_weights(load_in_8bit)
+        self.lm_head.prepare_weights()
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values: Optional[torch.Tensor] = None,
+        pre_allocate_past_size: Optional[int] = None,
+    ):
+        hidden_states, present = self.transformer(
+            input_ids,
+            position_ids,
+            cu_seqlens,
+            max_s,
+            past_key_values,
+            pre_allocate_past_size,
+        )
+        logits = self.lm_head(hidden_states)
+
+        if self.transformer.tp_embeddings:
+            # Logits are sharded, so we need to gather them
+            world_logits = [
+                torch.empty_like(logits) for _ in range(self.transformer.tp_world_size)
+            ]
+            torch.distributed.all_gather(
+                world_logits, logits, group=self.transformer.process_group
+            )
+            world_logits = torch.cat(world_logits, dim=1)
+
+            return world_logits, present
+
+        return logits, present
diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
new file mode 100644
index 0000000000000000000000000000000000000000..61ccca84d3cd3be8f21d976cf364ea0d78d01329
--- /dev/null
+++ b/server/text_generation_server/models/flash_causal_lm.py
@@ -0,0 +1,627 @@
+import torch
+import torch.distributed
+
+from torch.nn import functional as F
+
+from dataclasses import dataclass
+from opentelemetry import trace
+from transformers import AutoTokenizer, PreTrainedTokenizerBase, PreTrainedModel
+from typing import Optional, Tuple, List, Type, Union, Dict
+
+from text_generation_server.models import Model
+from text_generation_server.models.types import (
+    Batch,
+    PrefillTokens,
+    Generation,
+    GeneratedText,
+)
+from text_generation_server.pb import generate_pb2
+from text_generation_server.utils import (
+    NextTokenChooser,
+    StoppingCriteria,
+    Sampling,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+@dataclass
+class FlashCausalLMBatch(Batch):
+    batch_id: int
+    requests: List[generate_pb2.Request]
+    # request id -> idx in list mapping
+    requests_idx_mapping: Dict[int, int]
+
+    # Decoder values
+    input_ids: List[torch.Tensor]
+    position_ids: List[torch.Tensor]
+    # cumulative sequence lengths
+    cu_seqlens: List[int]
+    max_seqlen: int
+    past_key_values: Optional[Union[torch.Tensor, List[torch.Tensor]]]
+
+    # All tokens
+    all_input_ids: List[List[int]]
+    all_input_ids_tensor: List[torch.Tensor]
+
+    # Lengths of all generations present in the batch
+    input_lengths: List[int]
+    offsets: List[Optional[int]]
+    token_offsets: List[Optional[int]]
+
+    # Generation helpers
+    next_token_choosers: List[NextTokenChooser]
+    stopping_criterias: List[StoppingCriteria]
+
+    # Constant shared tensor, ref here just so that it's accessible in concatentate()
+    past_pad: Optional[torch.Tensor]
+
+    # Maximum number of tokens this batch will grow to
+    max_tokens: int
+
+    def to_pb(self) -> generate_pb2.Batch:
+        return generate_pb2.Batch(
+            id=self.batch_id,
+            requests=self.requests,
+            size=len(self),
+            max_tokens=self.max_tokens,
+        )
+
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        device: torch.device,
+    ) -> "FlashCausalLMBatch":
+        input_ids = []
+        position_ids = []
+        cu_seqlens = [0]
+        max_seqlen = 0
+
+        input_lengths = []
+        offsets = []
+        token_offsets = []
+        all_input_ids = []
+        all_input_ids_tensor = []
+        requests_idx_mapping = {}
+
+        next_token_choosers = []
+        stopping_criterias = []
+
+        # Cumulative length
+        cumulative_length = 0
+
+        max_tokens = 0
+
+        # Parse batch
+        for i, r in enumerate(pb.requests):
+            # request id -> idx in list mapping
+            requests_idx_mapping[r.id] = i
+
+            tokenized_input = tokenizer(
+                r.inputs, truncation=True, max_length=r.truncate
+            )["input_ids"]
+
+            input_length = len(tokenized_input)
+            max_seqlen = max(max_seqlen, input_length)
+            input_lengths.append(input_length)
+
+            offsets.append(None)
+            token_offsets.append(None)
+            all_input_ids.append(tokenized_input)
+
+            tokenized_input = torch.tensor(tokenized_input, device=device)
+            input_ids.append(tokenized_input)
+
+            # Position ids
+            position_ids.append(
+                torch.arange(0, input_length, dtype=torch.int32, device=device)
+            )
+
+            # Add cumulative lengths of all previous inputs
+            cu_seqlens.append(cumulative_length + input_length)
+
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
+
+            stopping_criteria = StoppingCriteria.from_pb(
+                r.stopping_parameters, tokenizer
+            )
+            max_new_tokens = stopping_criteria.max_new_tokens
+            stopping_criterias.append(stopping_criteria)
+
+            all_input_ids_tensor.append(
+                F.pad(tokenized_input, (0, stopping_criteria.max_new_tokens))
+            )
+
+            # Update
+            cumulative_length += input_length
+            max_tokens += input_length + max_new_tokens
+
+        return cls(
+            batch_id=pb.id,
+            requests=pb.requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            past_key_values=None,
+            input_lengths=input_lengths,
+            offsets=offsets,
+            token_offsets=token_offsets,
+            all_input_ids=all_input_ids,
+            all_input_ids_tensor=all_input_ids_tensor,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            past_pad=None,
+            max_tokens=max_tokens,
+        )
+
+    @tracer.start_as_current_span("filter")
+    def filter(self, requests: List[generate_pb2.Request]) -> "FlashCausalLMBatch":
+        if len(requests) == 0:
+            raise ValueError("Batch must have at least one request")
+        # We assume that if len(requests) == len(self) then the requests are the same
+        if len(requests) == len(self):
+            return self
+
+        single_request = len(requests) == 1
+
+        # Cumulative length
+        cumulative_length = 0
+
+        # New values after filtering
+        requests_idx_mapping = {}
+
+        input_ids = []
+        position_ids = []
+        cu_seqlens = [0]
+        max_seqlen = 0
+        past_key_values = []
+
+        all_input_ids = []
+        all_input_ids_tensor = []
+
+        input_lengths = []
+        offsets = []
+        token_offsets = []
+
+        next_token_choosers = []
+        stopping_criterias = []
+
+        max_tokens = 0
+
+        for i, r in enumerate(requests):
+            idx = self.requests_idx_mapping[r.id]
+            requests_idx_mapping[r.id] = i
+
+            # Get length
+            request_input_length = self.input_lengths[idx]
+
+            input_ids.append(self.input_ids[idx])
+            position_ids.append(self.position_ids[idx])
+            cu_seqlens.append(cumulative_length + request_input_length)
+            max_seqlen = max(max_seqlen, request_input_length)
+            # True index for past
+            past_key_values.append(self.past_key_values[2 * idx])
+
+            if not single_request:
+                # Add one padding
+                past_key_values.append(self.past_pad)
+
+            all_input_ids.append(self.all_input_ids[idx])
+            all_input_ids_tensor.append(self.all_input_ids_tensor[idx])
+
+            input_lengths.append(request_input_length)
+            offsets.append(self.offsets[idx])
+            token_offsets.append(self.token_offsets[idx])
+
+            next_token_choosers.append(self.next_token_choosers[idx])
+
+            stopping_criteria = self.stopping_criterias[idx]
+            stopping_criterias.append(stopping_criteria)
+
+            cumulative_length += request_input_length
+            max_tokens += request_input_length + (
+                stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
+            )
+
+        if single_request:
+            # Preallocate tensor for bs = 1 case
+            past_key_values = torch.nn.functional.pad(
+                past_key_values[0],
+                (
+                    0,
+                    0,
+                    0,
+                    0,
+                    0,
+                    0,
+                    0,
+                    stopping_criterias[0].max_new_tokens
+                    - stopping_criterias[0].current_tokens,
+                ),
+            )
+
+        return FlashCausalLMBatch(
+            batch_id=self.batch_id,
+            past_pad=self.past_pad,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            past_key_values=past_key_values,
+            input_lengths=input_lengths,
+            offsets=offsets,
+            token_offsets=token_offsets,
+            all_input_ids=all_input_ids,
+            all_input_ids_tensor=all_input_ids_tensor,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            max_tokens=max_tokens,
+        )
+
+    @classmethod
+    @tracer.start_as_current_span("concatenate")
+    def concatenate(cls, batches: List["FlashCausalLMBatch"]) -> "FlashCausalLMBatch":
+        # Batch attributes
+        requests = []
+        requests_idx_mapping = {}
+
+        input_ids = []
+        position_ids = []
+        cu_seqlens = [0]
+        max_seqlen = 0
+        past_key_values = []
+
+        all_input_ids = []
+        all_input_ids_tensor = []
+
+        input_lengths = []
+        offsets = []
+        token_offsets = []
+
+        next_token_choosers = []
+        stopping_criterias = []
+
+        # Cumulative length
+        cumulative_batch_size = 0
+        cumulative_length = 0
+        max_tokens = 0
+
+        for i, batch in enumerate(batches):
+            requests.extend(batch.requests)
+
+            if i == 0:
+                requests_idx_mapping = batch.requests_idx_mapping
+            else:
+                # We need to offset the mapping for each batch by the cumulative batch size
+                for k, v in batch.requests_idx_mapping.items():
+                    requests_idx_mapping[k] = v + cumulative_batch_size
+
+            input_ids.extend(batch.input_ids)
+            position_ids.extend(batch.position_ids)
+            # Add cumulative lengths of all previous inputs
+            cu_seqlens.extend([l + cumulative_length for l in batch.cu_seqlens[1:]])
+            max_seqlen = max(max_seqlen, batch.max_seqlen)
+
+            if len(batch) != 1:
+                past_key_values.extend(batch.past_key_values)
+            else:
+                # past was pre-allocated for this batch
+                # We need to slice to remove the padding
+                past_key_values.append(
+                    batch.past_key_values[:, : batch.input_lengths[0]]
+                )
+                # Add one padding
+                past_key_values.append(batch.past_pad)
+
+            all_input_ids.extend(batch.all_input_ids)
+            all_input_ids_tensor.extend(batch.all_input_ids_tensor)
+
+            input_lengths.extend(batch.input_lengths)
+            offsets.extend(batch.offsets)
+            token_offsets.extend(batch.token_offsets)
+
+            next_token_choosers.extend(batch.next_token_choosers)
+            stopping_criterias.extend(batch.stopping_criterias)
+
+            # Update
+            cumulative_length += batch.cu_seqlens[-1]
+            cumulative_batch_size += len(batch)
+            max_tokens += batch.max_tokens
+
+        return FlashCausalLMBatch(
+            batch_id=batches[0].batch_id,
+            past_pad=batches[0].past_pad,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            past_key_values=past_key_values,
+            input_lengths=input_lengths,
+            offsets=offsets,
+            token_offsets=token_offsets,
+            all_input_ids=all_input_ids,
+            all_input_ids_tensor=all_input_ids_tensor,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            max_tokens=max_tokens,
+        )
+
+    def __len__(self):
+        return len(self.requests)
+
+
+class FlashCausalLM(Model):
+    def __init__(
+        self,
+        model_cls: Type[PreTrainedModel],
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: bool = False,
+        decode_buffer: int = 3,
+    ):
+        self.past_pad = None
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            raise NotImplementedError("FlashCausalLM is only available on GPU")
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+        self.model = (
+            model_cls.from_pretrained(
+                model_id,
+                revision=revision,
+                torch_dtype=dtype,
+                load_in_8bit=quantize,
+            )
+            .eval()
+            .to(device)
+        )
+
+        super(FlashCausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=False,
+            dtype=dtype,
+            device=device,
+            decode_buffer=decode_buffer,
+        )
+
+    @property
+    def batch_type(self) -> Type[FlashCausalLMBatch]:
+        return FlashCausalLMBatch
+
+    def decode(self, generated_ids: Union[torch.Tensor, List[int]]) -> str:
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=True, cleanup_tokenization_spaces=False
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_s: int,
+        past_key_values: Optional = None,
+        pre_allocate_past_size: Optional[int] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Model Forward
+        return self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_s=max_s,
+            past_key_values=past_key_values,
+            pre_allocate_past_size=pre_allocate_past_size,
+        )
+
+    @tracer.start_as_current_span("generate_token")
+    def generate_token(
+        self, batch: FlashCausalLMBatch
+    ) -> Tuple[List[Generation], Optional[FlashCausalLMBatch]]:
+        # Shortcut when batch_size == 1
+        if len(batch) == 1:
+            input_ids = batch.input_ids[0].view(-1)
+            # No need to slice as flash attention will take care of it with cu_seqlens
+            past_key_values = batch.past_key_values
+        else:
+            # Concatenate tensors
+            input_ids = torch.cat(batch.input_ids).view(-1)
+            past_key_values = (
+                torch.cat(batch.past_key_values, dim=1)
+                if batch.past_key_values is not None
+                else None
+            )
+
+        # if prefill and bs == 1
+        if past_key_values is None and len(batch) == 1:
+            # Ask to pre-allocate kv to its max size
+            # == number of tokens + max_new_tokens
+            pre_allocate_past_size = (
+                batch.input_lengths[0] + batch.stopping_criterias[0].max_new_tokens
+            )
+        else:
+            pre_allocate_past_size = None
+
+        # Concatenate when prefill, torch.tensor when decode
+        position_ids = (
+            torch.tensor(batch.position_ids, device=self.device)
+            if batch.past_key_values is not None
+            else torch.cat(batch.position_ids)
+        )
+        cu_seqlens = torch.tensor(
+            batch.cu_seqlens, device=self.device, dtype=torch.int32
+        )
+
+        out, present = self.forward(
+            input_ids,
+            position_ids,
+            cu_seqlens,
+            batch.max_seqlen,
+            past_key_values,
+            pre_allocate_past_size,
+        )
+
+        # Initialize past_key_values in prefill
+        if batch.past_key_values is None:
+            # Initialize past padding tensor
+            if self.past_pad is None:
+                self.past_pad = present.new_zeros(
+                    present.shape[0], 1, *present.shape[2:]
+                )
+            # Set in batch in case it needs to be used later in concatenate()
+            batch.past_pad = self.past_pad
+            if len(batch) == 1:
+                # present is already pre-padded
+                batch.past_key_values = present
+            else:
+                # Add padding after each sequence
+                # This will have the correct shape after the final past_key_values concatenation before the model
+                # forward
+                batch.past_key_values = [None, self.past_pad] * len(batch)
+
+        # Cumulative length
+        cumulative_length = 0
+
+        # Results
+        generations: List[Generation] = []
+        stopped = True
+
+        # Zipped iterator
+        iterator = zip(
+            batch.requests,
+            batch.input_lengths,
+            batch.offsets,
+            batch.token_offsets,
+            batch.next_token_choosers,
+            batch.stopping_criterias,
+            batch.all_input_ids,
+            batch.all_input_ids_tensor,
+        )
+
+        # For each member of the batch
+        for i, (
+            request,
+            input_length,
+            offset,
+            token_offset,
+            next_token_chooser,
+            stopping_criteria,
+            all_input_ids,
+            all_input_ids_tensor,
+        ) in enumerate(iterator):
+            # Indexing metadata
+            start_index = cumulative_length
+            end_index = cumulative_length + input_length
+
+            prefill = stopping_criteria.current_tokens == 0
+            if prefill:
+                # Prefill mode
+                # out is of shape [cumulative_sequence_lengths, vocab_size]
+                logits = out[start_index:end_index]
+            else:
+                # Decode mode
+                # out is of shape [batch_size, vocab_size]
+                logits = out[i].unsqueeze(0)
+
+            # Select next token
+            next_token_id, logprobs = next_token_chooser(
+                all_input_ids_tensor[None, :input_length], logits
+            )
+            next_token_id_squeezed = next_token_id.squeeze()
+            next_token_id_item = next_token_id_squeezed.item()
+
+            # Append next token to all tokens
+            all_input_ids.append(next_token_id_item)
+            all_input_ids_tensor[input_length] = next_token_id_item
+
+            # Generated token
+            next_token_logprob = logprobs[-1, next_token_id_item]
+            next_token_text, offset, token_offset = self.decode_token(
+                all_input_ids,
+                offset,
+                token_offset,
+            )
+
+            # Evaluate stopping criteria
+            stop, reason = stopping_criteria(
+                next_token_id_item,
+                next_token_text,
+            )
+
+            if stop:
+                # Decode generated tokens
+                output_text = self.decode(
+                    all_input_ids[-stopping_criteria.current_tokens :]
+                )
+                # Get seed
+                if isinstance(next_token_chooser.choice, Sampling):
+                    seed = next_token_chooser.choice.seed
+                else:
+                    seed = None
+
+                generated_text = GeneratedText(
+                    output_text, stopping_criteria.current_tokens, reason, seed
+                )
+            else:
+                stopped = False
+                generated_text = None
+
+            # Prefill
+            if prefill:
+                # Remove generated token to only have prefill and add nan for first prompt token
+                prefill_logprobs = [float("nan")] + logprobs.gather(
+                    1, all_input_ids_tensor[1:input_length].unsqueeze(1)
+                ).squeeze(1)[:-1].tolist()
+                prefill_token_ids = all_input_ids[:-1]
+                prefill_texts = self.tokenizer.batch_decode(
+                    prefill_token_ids,
+                    clean_up_tokenization_spaces=False,
+                    skip_special_tokens=False,
+                )
+                prefill_tokens = PrefillTokens(
+                    prefill_token_ids, prefill_logprobs, prefill_texts
+                )
+            else:
+                prefill_tokens = None
+
+            generation = Generation(
+                request.id,
+                prefill_tokens,
+                next_token_id_item,
+                next_token_logprob,
+                next_token_text,
+                next_token_id_item in self.all_special_ids,
+                generated_text,
+            )
+
+            generations.append(generation)
+            cumulative_length += input_length
+            new_input_length = input_length + 1
+
+            # Update values
+            batch.input_ids[i] = next_token_id
+            batch.position_ids[i] = input_length
+            batch.input_lengths[i] = new_input_length
+            batch.offsets[i] = offset
+            batch.token_offsets[i] = token_offset
+            batch.all_input_ids[i] = all_input_ids
+            batch.all_input_ids_tensor[i] = all_input_ids_tensor
+            batch.max_seqlen = max(batch.max_seqlen, new_input_length)
+            if len(batch) != 1:
+                # Add each sequence before its padding
+                batch.past_key_values[i * 2] = present[:, start_index:end_index]
+            # Cumulative sum
+            batch.cu_seqlens[(i + 1)] = batch.cu_seqlens[i] + new_input_length
+
+        # No need to return a batch if we know that all requests stopped
+        return generations, batch if not stopped else None
diff --git a/server/text_generation_server/models/flash_llama.py b/server/text_generation_server/models/flash_llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..e640113b4ea5efdd33a4daa3dc99d4e7f1e3329a
--- /dev/null
+++ b/server/text_generation_server/models/flash_llama.py
@@ -0,0 +1,304 @@
+import torch
+import torch.distributed
+
+from accelerate import init_empty_weights
+from opentelemetry import trace
+from pathlib import Path
+from safetensors import safe_open
+from transformers import AutoConfig
+from transformers.models.llama import LlamaTokenizer
+from typing import Optional, List
+
+from text_generation_server.models import FlashCausalLM
+from text_generation_server.models.custom_modeling.flash_llama_modeling import (
+    FlashLlamaForCausalLM,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+    TensorParallelColumnLinear,
+)
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    download_weights,
+    weight_hub_files,
+    LocalEntryNotFoundError,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+class FlashLlama(FlashCausalLM):
+    def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
+        self.past_pad = None
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            raise NotImplementedError("FlashLlama is only available on GPU")
+
+        tokenizer = LlamaTokenizer.from_pretrained(
+            model_id,
+            revision=revision,
+            padding_side="left",
+            truncation_side="left",
+        )
+
+        config = AutoConfig.from_pretrained(
+            model_id,
+            revision=revision,
+        )
+
+        # We do not use from_pretrained as we modified the model internal module layout
+        try:
+            filenames = weight_files(model_id, revision, ".bin")
+        # Local files not found
+        except LocalEntryNotFoundError:
+            hub_files = weight_hub_files(model_id, revision, ".bin")
+            filenames = download_weights(hub_files, model_id, revision)
+
+        with init_empty_weights():
+            model = FlashLlamaForCausalLM(config)
+
+        self.load_weights(model, filenames, quantize, device, dtype)
+        self.model = model.eval().to(device)
+
+        super(FlashCausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=False,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[Path],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        for filename in filenames:
+            state_dict = torch.load(filename, map_location="cpu")
+            for key, value in state_dict.items():
+                value = value.to(device if not quantize else "cpu").to(dtype)
+
+                layer_name = ".".join(key.split(".")[:4])
+
+                # Fused qkv
+                if "q_proj" in key or "k_proj" in key or "v_proj" in key:
+                    final_key = layer_name + ".query_key_value.weight"
+
+                # Fused gate and up projs
+                elif "gate_proj" in key or "up_proj" in key:
+                    final_key = layer_name + ".gate_up_proj.weight"
+                else:
+                    final_key = key
+
+                module_name, param_name = final_key.rsplit(".", 1)
+                module = model.get_submodule(module_name)
+
+                try:
+                    current_parameter_tensor = module._parameters[param_name]
+                except KeyError:
+                    current_parameter_tensor = None
+
+                if current_parameter_tensor is not None:
+                    if current_parameter_tensor.device == torch.device("meta"):
+                        # Init qkv
+                        if "query_key_value" in final_key:
+                            module._parameters[param_name] = value.new_empty(
+                                (value.shape[0] * 3, value.shape[1])
+                            )
+                        # Init gate and up proj
+                        elif "gate_up_proj" in final_key:
+                            module._parameters[param_name] = value.new_empty(
+                                (value.shape[0] * 2, value.shape[1])
+                            )
+
+                    # Copy to correct slice
+                    if "q_proj" in key:
+                        module._parameters[param_name][: value.shape[0]] = value
+                    elif "k_proj" in key:
+                        module._parameters[param_name][
+                            value.shape[0] : value.shape[0] * 2
+                        ] = value
+                    elif "v_proj" in key:
+                        module._parameters[param_name][value.shape[0] * 2 :] = value
+                    elif "gate_proj" in key:
+                        module._parameters[param_name][: value.shape[0]] = value
+                    elif "up_proj" in key:
+                        module._parameters[param_name][value.shape[0] :] = value
+                    else:
+                        if current_parameter_tensor.shape != value.shape:
+                            raise ValueError(
+                                f"Name {final_key} -- Current {current_parameter_tensor.shape} and got {value.shape}"
+                            )
+                        module._parameters[param_name] = value
+                else:
+                    module._buffers[param_name] = value
+
+                del value
+
+        torch.cuda.empty_cache()
+        model.post_load_weights(quantize)
+
+
+class FlashLlamaSharded(FlashLlama):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.past_pad = None
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            raise NotImplementedError("FlashLlama is only available on GPU")
+
+        tokenizer = LlamaTokenizer.from_pretrained(
+            model_id,
+            revision=revision,
+            padding_side="left",
+            truncation_side="left",
+        )
+
+        config = AutoConfig.from_pretrained(
+            model_id,
+            revision=revision,
+        )
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = FlashLlamaForCausalLM(config, process_group=self.process_group)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+        )
+        self.model = model.eval().to(device)
+        torch.distributed.barrier(group=self.process_group)
+        super(FlashCausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=False,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+    ):
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for name in f.keys():
+                    slice_ = f.get_slice(name)
+
+                    layer_name = ".".join(name.split(".")[:4])
+
+                    # Fused qkv
+                    if "q_proj" in name or "k_proj" in name or "v_proj" in name:
+                        final_name = layer_name + ".query_key_value.weight"
+
+                    # Fused gate and up projs
+                    elif "gate_proj" in name or "up_proj" in name:
+                        final_name = layer_name + ".gate_up_proj.weight"
+                    else:
+                        final_name = name
+
+                    module_name, param_name = final_name.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif isinstance(module, TensorParallelRowLinear):
+                        size = slice_.get_shape()[1]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[:, start:stop]
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif name == "lm_head.weight" and model.model.tp_embeddings:
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    else:
+                        try:
+                            tensor = slice_[:]
+                        except:
+                            tensor = f.get_tensor(name)
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    try:
+                        current_parameter_tensor = module._parameters[param_name]
+                    except KeyError:
+                        current_parameter_tensor = None
+
+                    if current_parameter_tensor is not None:
+                        if current_parameter_tensor.device == torch.device("meta"):
+                            # Init qkv
+                            if "query_key_value" in final_name:
+                                module._parameters[param_name] = tensor.new_empty(
+                                    (tensor.shape[0] * 3, tensor.shape[1])
+                                )
+                            # Init gate and up proj
+                            elif "gate_up_proj" in final_name:
+                                module._parameters[param_name] = tensor.new_empty(
+                                    (tensor.shape[0] * 2, tensor.shape[1])
+                                )
+
+                        # Init gate and up proj
+                        if "q_proj" in name:
+                            module._parameters[param_name][: tensor.shape[0]] = tensor
+                        elif "k_proj" in name:
+                            module._parameters[param_name][
+                                tensor.shape[0] : tensor.shape[0] * 2
+                            ] = tensor
+                        elif "v_proj" in name:
+                            module._parameters[param_name][
+                                tensor.shape[0] * 2 :
+                            ] = tensor
+                        elif "gate_proj" in name:
+                            module._parameters[param_name][: tensor.shape[0]] = tensor
+                        elif "up_proj" in name:
+                            module._parameters[param_name][tensor.shape[0] :] = tensor
+                        else:
+                            if current_parameter_tensor.shape != tensor.shape:
+                                raise ValueError(
+                                    f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
+                                )
+
+                            module._parameters[param_name] = tensor
+
+                    else:
+                        module._buffers[param_name] = tensor
+        torch.cuda.empty_cache()
+        model.post_load_weights(quantize)
diff --git a/server/text_generation_server/models/flash_neox.py b/server/text_generation_server/models/flash_neox.py
new file mode 100644
index 0000000000000000000000000000000000000000..eae584ac59a1bd6bde05a83f15747e60cfb4fddd
--- /dev/null
+++ b/server/text_generation_server/models/flash_neox.py
@@ -0,0 +1,152 @@
+import torch
+import torch.distributed
+
+from accelerate import init_empty_weights
+from opentelemetry import trace
+from safetensors import safe_open
+from transformers import AutoTokenizer, AutoConfig
+from typing import Optional, List
+
+from text_generation_server.models import FlashCausalLM
+from text_generation_server.models.custom_modeling.flash_neox_modeling import (
+    FlashGPTNeoXForCausalLM,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+    TensorParallelColumnLinear,
+)
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+class FlashNeoX(FlashCausalLM):
+    def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
+        super(FlashNeoX, self).__init__(
+            FlashGPTNeoXForCausalLM, model_id, revision, quantize
+        )
+
+
+class FlashNeoXSharded(FlashNeoX):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.past_pad = None
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            raise NotImplementedError("FlashNeoX is only available on GPU")
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+
+        config = AutoConfig.from_pretrained(
+            model_id,
+            revision=revision,
+        )
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = FlashGPTNeoXForCausalLM(config, self.process_group)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+        )
+        self.model = model.eval().to(device)
+        torch.distributed.barrier(group=self.process_group)
+        super(FlashCausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=False,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+    ):
+        parameters = dict(model.named_parameters())
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for name in f.keys():
+                    module_name, param_name = name.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+
+                    current_parameter_tensor = parameters.get(name, None)
+
+                    slice_ = f.get_slice(name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif isinstance(module, TensorParallelRowLinear):
+                        if param_name == "weight":
+                            size = slice_.get_shape()[1]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = slice_[:, start:stop]
+                        else:
+                            tensor = slice_[:]
+                            # XXX: Hack for Rowlinear to add the bias only once.
+                            if rank != 0:
+                                tensor = torch.zeros_like(tensor)
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif name == "embed_out.weight" and model.gpt_neox.tp_embeddings:
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    else:
+                        try:
+                            tensor = slice_[:]
+                        except:
+                            tensor = f.get_tensor(name)
+
+                    if (
+                        current_parameter_tensor is not None
+                        and current_parameter_tensor.shape != tensor.shape
+                    ):
+                        raise ValueError(
+                            f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
+                        )
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    if current_parameter_tensor is not None:
+                        module._parameters[param_name] = tensor
+                    else:
+                        module._buffers[param_name] = tensor
+        model.post_load_weights(quantize)
diff --git a/server/text_generation_server/models/flash_santacoder.py b/server/text_generation_server/models/flash_santacoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa1bdfb54ddcce22a051c23d244984794a96d2ad
--- /dev/null
+++ b/server/text_generation_server/models/flash_santacoder.py
@@ -0,0 +1,376 @@
+import torch
+import torch.distributed
+
+from accelerate import init_empty_weights
+from opentelemetry import trace
+from safetensors import safe_open
+from pathlib import Path
+from transformers import AutoTokenizer, GPT2Config
+from typing import Optional, List
+
+from text_generation_server.models import FlashCausalLM
+from text_generation_server.models.custom_modeling.flash_santacoder_modeling import (
+    FlashSantacoderForCausalLM,
+    TensorParallelRowLinear,
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+)
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    download_weights,
+    weight_hub_files,
+    LocalEntryNotFoundError,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+class FlashSantacoder(FlashCausalLM):
+    def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
+        self.past_pad = None
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            raise NotImplementedError("FlashSantacoder is only available on GPU")
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+
+        config = GPT2Config.from_pretrained(
+            model_id,
+            revision=revision,
+        )
+
+        # We do not use from_pretrained as we modified the model internal module layout
+        try:
+            filenames = weight_files(model_id, revision, ".bin")
+        # Local files not found
+        except LocalEntryNotFoundError:
+            hub_files = weight_hub_files(model_id, revision, ".bin")
+            filenames = download_weights(hub_files, model_id, revision)
+
+        with init_empty_weights():
+            model = FlashSantacoderForCausalLM(config)
+
+        self.load_weights(
+            model,
+            filenames,
+            quantize,
+            device,
+            dtype,
+            config.architectures[0].startswith("GPT2"),
+        )
+        self.model = model.eval().to(device)
+
+        super(FlashCausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=False,
+            dtype=dtype,
+            device=device,
+            decode_buffer=1,
+        )
+
+    @staticmethod
+    def load_weights(
+        model: FlashSantacoderForCausalLM,
+        filenames: List[Path],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        transpose: bool,
+    ):
+        for filename in filenames:
+            state_dict = torch.load(filename, map_location="cpu")
+            for key, value in state_dict.items():
+                value = value.to(device if not quantize else "cpu").to(dtype)
+
+                layer_name = ".".join(key.split(".")[:4])
+
+                # Fused qkv
+                if "q_attn.weight" in key or "kv_attn.weight" in key:
+                    final_key = layer_name + ".c_attn.weight"
+                elif "q_attn.bias" in key or "kv_attn.bias" in key:
+                    final_key = layer_name + ".c_attn.bias"
+
+                else:
+                    final_key = key
+
+                module_name, param_name = final_key.rsplit(".", 1)
+                module = model.get_submodule(module_name)
+
+                try:
+                    current_parameter_tensor = module._parameters[param_name]
+                except KeyError:
+                    current_parameter_tensor = None
+
+                if current_parameter_tensor is not None:
+                    if transpose and (
+                        "c_fc.weight" in key
+                        or "c_proj.weight" in key
+                        or "q_attn.weight" in key
+                        or "kv_attn.weight" in key
+                        or "c_attn.weight" in key
+                    ):
+                        # Tranpose as we use nn.Linear instead of Conv1D
+                        value = value.T
+
+                    if current_parameter_tensor.device == torch.device("meta"):
+                        # Init qkv
+                        if "c_attn.weight" in final_key:
+                            module._parameters[param_name] = value.new_empty(
+                                (
+                                    model.transformer.head_size
+                                    * (model.transformer.num_heads + 2),
+                                    value.shape[1],
+                                )
+                            )
+                        elif "c_attn.bias" in final_key:
+                            module._parameters[param_name] = value.new_empty(
+                                (
+                                    model.transformer.head_size
+                                    * (model.transformer.num_heads + 2)
+                                )
+                            )
+
+                    # Copy to correct slice
+                    if "q_attn.weight" in key:
+                        module._parameters[param_name][: value.shape[0]] = value
+                    elif "q_attn.bias" in key:
+                        module._parameters[param_name][: value.shape[0]] = value
+                    elif "kv_attn.weight" in key:
+                        module._parameters[param_name][
+                            model.transformer.head_size * model.transformer.num_heads :
+                        ] = value
+                    elif "kv_attn.bias" in key:
+                        module._parameters[param_name][
+                            model.transformer.head_size * model.transformer.num_heads :
+                        ] = value
+                    else:
+                        if current_parameter_tensor.shape != value.shape:
+                            raise ValueError(
+                                f"Name {final_key} -- Current {current_parameter_tensor.shape} and got {value.shape}"
+                            )
+                        module._parameters[param_name] = value
+                else:
+                    module._buffers[param_name] = value
+
+                del value
+
+        torch.cuda.empty_cache()
+        model.post_load_weights(quantize)
+
+    def decode(self, generated_ids: List[int]) -> str:
+        # Do not skip special tokens as they are used for custom parsing rules of the generated text
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=False, cleanup_tokenization_spaces=False
+        )
+
+
+class FlashSantacoderSharded(FlashSantacoder):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.past_pad = None
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            raise NotImplementedError("FlashSantacoderSharded is only available on GPU")
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+
+        config = GPT2Config.from_pretrained(
+            model_id,
+            revision=revision,
+        )
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = FlashSantacoderForCausalLM(config, self.process_group)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+            transpose=config.architectures[0].startswith("GPT2"),
+        )
+        self.model = model.eval().to(device)
+        torch.distributed.barrier(group=self.process_group)
+        super(FlashCausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=False,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+        transpose: bool,
+    ):
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for key in f.keys():
+                    slice_ = f.get_slice(key)
+
+                    layer_name = ".".join(key.split(".")[:4])
+
+                    # Fused qkv
+                    if "q_attn.weight" in key or "kv_attn.weight" in key:
+                        final_key = layer_name + ".c_attn.weight"
+                    elif "q_attn.bias" in key or "kv_attn.bias" in key:
+                        final_key = layer_name + ".c_attn.bias"
+                    else:
+                        final_key = key
+
+                    module_name, param_name = final_key.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        dim = 1 if transpose and "weight" in param_name else 0
+                        size = slice_.get_shape()[dim]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = (
+                            slice_[start:stop] if dim == 0 else slice_[:, start:stop]
+                        )
+                    elif isinstance(module, TensorParallelRowLinear):
+                        if param_name == "weight":
+                            dim = 0 if transpose else 1
+                            size = slice_.get_shape()[dim]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = (
+                                slice_[start:stop]
+                                if dim == 0
+                                else slice_[:, start:stop]
+                            )
+                        else:
+                            tensor = slice_[:]
+                            # XXX: Hack for Rowlinear to add the bias only once.
+                            if rank != 0:
+                                tensor = torch.zeros_like(tensor)
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif key == "lm_head.weight" and model.transformer.tp_embeddings:
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    else:
+                        try:
+                            tensor = slice_[:]
+                        except:
+                            tensor = f.get_tensor(key)
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    try:
+                        current_parameter_tensor = module._parameters[param_name]
+                    except KeyError:
+                        current_parameter_tensor = None
+
+                    if current_parameter_tensor is not None:
+                        if transpose and (
+                            "c_fc.weight" in key
+                            or "c_proj.weight" in key
+                            or "q_attn.weight" in key
+                            or "kv_attn.weight" in key
+                            or "c_attn.weight" in key
+                        ):
+                            # Tranpose as we use nn.Linear instead of Conv1D
+                            tensor = tensor.T
+
+                        if current_parameter_tensor.device == torch.device("meta"):
+                            # Init qkv
+                            if "c_attn.weight" in final_key:
+                                module._parameters[param_name] = tensor.new_empty(
+                                    (
+                                        model.transformer.head_size
+                                        * (model.transformer.num_heads + 2),
+                                        tensor.shape[1],
+                                    )
+                                )
+                            elif "c_attn.bias" in final_key:
+                                module._parameters[param_name] = tensor.new_empty(
+                                    (
+                                        model.transformer.head_size
+                                        * (model.transformer.num_heads + 2)
+                                    )
+                                )
+
+                        # Copy to correct slice
+                        if "q_attn" in key:
+                            size = tensor.shape[0]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = tensor[start:stop]
+                            module._parameters[param_name][: tensor.shape[0]] = tensor
+                        elif "kv_attn.weight" in key:
+                            module._parameters[param_name][
+                                model.transformer.head_size
+                                * model.transformer.num_heads :
+                            ] = tensor
+                        elif "kv_attn.bias" in key:
+                            module._parameters[param_name][
+                                model.transformer.head_size
+                                * model.transformer.num_heads :
+                            ] = tensor
+                        elif "c_attn" in key:
+                            # Slice q_tensor by shard
+                            q_tensor = tensor[: -2 * model.transformer.head_size]
+                            block_size = q_tensor.shape[0] // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            q_tensor = q_tensor[start:stop]
+
+                            module._parameters[param_name][
+                                : q_tensor.shape[0]
+                            ] = q_tensor
+
+                            # Kv tensor is copied for every shard
+                            kv_tensor = tensor[-2 * model.transformer.head_size :]
+                            module._parameters[param_name][
+                                q_tensor.shape[0] :
+                            ] = kv_tensor
+                        else:
+                            if current_parameter_tensor.shape != tensor.shape:
+                                raise ValueError(
+                                    f"Name {key} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
+                                )
+
+                            module._parameters[param_name] = tensor
+                    else:
+                        module._buffers[param_name] = tensor
+        torch.cuda.empty_cache()
+        model.post_load_weights(quantize)
diff --git a/server/text_generation_server/models/galactica.py b/server/text_generation_server/models/galactica.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1ec1566cca3369036389fad2fb57b614f78a612
--- /dev/null
+++ b/server/text_generation_server/models/galactica.py
@@ -0,0 +1,373 @@
+import re
+import torch
+import torch.distributed
+
+from typing import List, Optional, Type, Tuple
+
+from accelerate import init_empty_weights
+from safetensors import safe_open
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+    PreTrainedTokenizerBase,
+)
+from transformers.models.opt.parallel_layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+)
+
+from text_generation_server.models import CausalLM
+from text_generation_server.models.causal_lm import CausalLMBatch
+from text_generation_server.pb import generate_pb2
+from text_generation_server.models.opt import OPT
+from text_generation_server.utils import (
+    NextTokenChooser,
+    StoppingCriteria,
+    initialize_torch_distributed,
+    weight_files,
+)
+
+HAS_BITS_AND_BYTES = True
+try:
+    import bitsandbytes as bnb
+    from bitsandbytes.nn import Int8Params
+except Exception as e:
+    HAS_BITS_AND_BYTES = False
+
+
+# CREDIT: Papers with code => https://github.com/paperswithcode/galai/blob/main/galai/utils.py
+
+# we split individual characters inside special tokens like [START_DNA]
+CUSTOM_SEQ_RE = re.compile(r"(\[START_(DNA|SMILES|I_SMILES|AMINO)])(.*?)(\[END_\2])")
+
+# token added to implement a custom sequence tokenization. This token is added at
+# corpus cleaning step and removed in pretokenization. The digits are added to increase the chance
+# that they do not occur in the corpus. The digits are escaped so that the token does not appear
+# literally in the source code in case we ever include it in the training data.
+SPLIT_MARKER = f"SPL{1}T-TH{1}S-Pl3A5E"
+
+
+def _insert_split_marker(m: re.Match):
+    """
+    Applies split marker based on a regex match of special tokens such as
+    [START_DNA].
+    Parameters
+    ----------
+    n : str
+        Input text to split
+    Returns
+    ----------
+    str - the text with the split token added
+    """
+    start_token, _, sequence, end_token = m.groups()
+    sequence = re.sub(r"(.)", rf"{SPLIT_MARKER}\1", sequence, flags=re.DOTALL)
+    return f"{start_token}{sequence}{SPLIT_MARKER}{end_token}"
+
+
+def escape_custom_split_sequence(text):
+    """
+    Applies custom splitting to the text for GALILEO's tokenization
+    Parameters
+    ----------
+    text : str
+        Input text to split
+    Returns
+    ----------
+    str - the text with the split token added
+    """
+    return CUSTOM_SEQ_RE.sub(_insert_split_marker, text)
+
+
+# END CREDIT
+
+
+class GalacticaCausalLMBatch(CausalLMBatch):
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        device: torch.device,
+    ) -> "GalacticaCausalLMBatch":
+        inputs = []
+        next_token_choosers = []
+        stopping_criterias = []
+        offsets = []
+        token_offsets = []
+        requests_idx_mapping = {}
+
+        # Parse batch
+        max_truncation = 0
+        padding_right_offset = 0
+        max_decode_tokens = 0
+        for i, r in enumerate(pb.requests):
+            requests_idx_mapping[r.id] = i
+            # Add escape_custom_split_sequence to the CausalLMBatch logic
+            inputs.append(escape_custom_split_sequence(r.inputs))
+            offsets.append(None)
+            token_offsets.append(None)
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
+            stopping_criteria = StoppingCriteria.from_pb(
+                r.stopping_parameters, tokenizer
+            )
+            stopping_criterias.append(stopping_criteria)
+            max_truncation = max(max_truncation, r.truncate)
+            max_decode_tokens += stopping_criteria.max_new_tokens
+            padding_right_offset = max(
+                padding_right_offset, stopping_criteria.max_new_tokens
+            )
+
+        tokenized_inputs = tokenizer(
+            inputs,
+            return_tensors="pt",
+            padding=True,
+            return_token_type_ids=False,
+            truncation=True,
+            max_length=max_truncation,
+        ).to(device)
+
+        input_lengths = tokenized_inputs["attention_mask"].sum(1)
+        max_input_length = input_lengths.max()
+
+        input_ids = tokenized_inputs["input_ids"]
+        # Allocate maximum attention_mask
+        attention_mask = input_ids.new_zeros(
+            (pb.size, max_input_length + padding_right_offset)
+        )
+        # Copy tokenizer attention_mask into fully allocated attention_mask
+        attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
+
+        position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
+        position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
+        all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
+
+        max_tokens = len(inputs) * max_input_length + max_decode_tokens
+
+        return cls(
+            batch_id=pb.id,
+            requests=pb.requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=None,
+            all_input_ids=list(all_input_ids),
+            input_lengths=input_lengths.tolist(),
+            offsets=offsets,
+            token_offsets=token_offsets,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            max_input_length=max_input_length.item(),
+            padding_right_offset=padding_right_offset,
+            max_tokens=max_tokens,
+        )
+
+
+class Galactica(OPT):
+    @property
+    def batch_type(self) -> Type[CausalLMBatch]:
+        return GalacticaCausalLMBatch
+
+    def decode(self, generated_ids: List[int]) -> str:
+        # Do not skip special tokens as they are used for custom parsing rules of the generated text
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=False, cleanup_tokenization_spaces=False
+        )
+
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
+        """Overwrite forward to ignore position_ids"""
+
+        # Model Forward
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+        return outputs.logits, outputs.past_key_values
+
+
+class GalacticaSharded(Galactica):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+
+        config = AutoConfig.from_pretrained(
+            model_id, revision=revision, tp_parallel=True
+        )
+        tokenizer.pad_token_id = config.pad_token_id
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = AutoModelForCausalLM.from_config(config)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+        )
+        self.model = model.eval()
+        torch.distributed.barrier(group=self.process_group)
+        super(CausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+    ):
+        parameters = dict(model.named_parameters())
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for name in f.keys():
+                    if name == "lm_head.weight":
+                        continue
+
+                    module_name, param_name = name.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+                    current_tensor = parameters[name]
+
+                    slice_ = f.get_slice(name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif isinstance(module, TensorParallelRowLinear):
+                        if param_name == "weight":
+                            size = slice_.get_shape()[1]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = slice_[:, start:stop]
+                        else:
+                            tensor = slice_[:]
+                            # XXX: Hack for Rowlinear to add the bias only once.
+                            if rank != 0:
+                                tensor = torch.zeros_like(tensor)
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    else:
+                        tensor = slice_[:]
+
+                    if current_tensor.shape != tensor.shape:
+                        raise ValueError(
+                            f"Name {name} -- Current {current_tensor.shape} and got {tensor.shape}"
+                        )
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    if quantize:
+                        if not HAS_BITS_AND_BYTES:
+                            raise ImportError(
+                                "bitsandbytes is not available on your machine either because it is not installed "
+                                "or you don't have a GPU.\n"
+                                "You can install it with `pip install bitsandbytes`."
+                            )
+
+                        if (
+                            type(module)
+                            in [TensorParallelRowLinear, TensorParallelColumnLinear]
+                            and param_name == "weight"
+                        ):
+                            tensor = Int8Params(
+                                tensor,
+                                has_fp16_weights=False,
+                                requires_grad=False,
+                            ).to(device)
+                            state = bnb.MatmulLtState()
+                            state.threshold = 6.0
+                            state.has_fp16_weights = False
+                            state.memory_efficient_backward = False
+                            state.use_pool = True
+                            state.CB = tensor.CB
+                            state.SCB = tensor.SCB
+                            tensor.CB = None
+                            tensor.SCB = None
+
+                            def replace_linear(state):
+                                def linear(input, weight, bias):
+                                    out = bnb.matmul(
+                                        input,
+                                        weight,
+                                        state=state,
+                                        threshold=state.threshold,
+                                        bias=bias,
+                                    )
+
+                                    if state.CB is not None:
+                                        # we converted 8-bit row major to turing/ampere format
+                                        # in the first inference pass
+                                        # we no longer need the row-major weight
+                                        del state.CB
+                                        weight.data = state.CxB
+
+                                    return out
+
+                                return linear
+
+                            module.linear = replace_linear(state)
+
+                        else:
+                            tensor = tensor.to(device)
+
+                    module._parameters[param_name] = tensor
+                    if name == "model.decoder.embed_tokens.weight":
+                        model.lm_head._parameters["weight"] = tensor
+
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+
+        # Logits are sharded, so we need to gather them
+        logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
+        torch.distributed.all_gather(logits, outputs.logits, group=self.process_group)
+        logits = torch.cat(logits, dim=2)
+
+        return logits, outputs.past_key_values
diff --git a/server/text_generation_server/models/gpt_neox.py b/server/text_generation_server/models/gpt_neox.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b5fe2cc3364091f9a4c012953bdd82872776601
--- /dev/null
+++ b/server/text_generation_server/models/gpt_neox.py
@@ -0,0 +1,232 @@
+import torch
+import torch.distributed
+
+from typing import List, Optional
+
+from accelerate import init_empty_weights
+from safetensors import safe_open
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+)
+from transformers.models.gpt_neox.parallel_layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+)
+
+from text_generation_server.models import CausalLM
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+)
+
+HAS_BITS_AND_BYTES = True
+try:
+    import bitsandbytes as bnb
+    from bitsandbytes.nn import Int8Params
+except Exception as e:
+    HAS_BITS_AND_BYTES = False
+
+
+class GPTNeoxSharded(CausalLM):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+        tokenizer.pad_token = tokenizer.eos_token
+
+        config = AutoConfig.from_pretrained(
+            model_id, revision=revision, tp_parallel=True
+        )
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = AutoModelForCausalLM.from_config(config)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+        )
+        self.model = model.eval()
+        torch.distributed.barrier(group=self.process_group)
+        super(CausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+    ):
+        parameters = dict(model.named_parameters())
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for name in f.keys():
+                    module_name, param_name = name.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+
+                    current_parameter_tensor = parameters.get(name, None)
+
+                    slice_ = f.get_slice(name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif isinstance(module, TensorParallelRowLinear):
+                        if param_name == "weight":
+                            size = slice_.get_shape()[1]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = slice_[:, start:stop]
+                        else:
+                            tensor = slice_[:]
+                            # XXX: Hack for Rowlinear to add the bias only once.
+                            if rank != 0:
+                                tensor = torch.zeros_like(tensor)
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif name == "embed_out.weight" and model.gpt_neox.tp_embeddings:
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    else:
+                        try:
+                            tensor = slice_[:]
+                        except:
+                            tensor = f.get_tensor(name)
+
+                    if (
+                        current_parameter_tensor is not None
+                        and current_parameter_tensor.shape != tensor.shape
+                    ):
+                        raise ValueError(
+                            f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
+                        )
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    if quantize:
+                        if not HAS_BITS_AND_BYTES:
+                            raise ImportError(
+                                "bitsandbytes is not available on your machine either because it is not installed "
+                                "or you don't have a GPU.\n"
+                                "You can install it with `pip install bitsandbytes`."
+                            )
+
+                        if (
+                            type(module)
+                            in [TensorParallelRowLinear, TensorParallelColumnLinear]
+                            and param_name == "weight"
+                        ):
+                            tensor = Int8Params(
+                                tensor,
+                                has_fp16_weights=False,
+                                requires_grad=False,
+                            ).to(device)
+                            state = bnb.MatmulLtState()
+                            state.threshold = 6.0
+                            state.has_fp16_weights = False
+                            state.memory_efficient_backward = False
+                            state.use_pool = True
+                            state.CB = tensor.CB
+                            state.SCB = tensor.SCB
+                            tensor.CB = None
+                            tensor.SCB = None
+
+                            def replace_linear(state):
+                                def linear(input, weight, bias):
+                                    out = bnb.matmul(
+                                        input,
+                                        weight,
+                                        state=state,
+                                        threshold=state.threshold,
+                                        bias=bias,
+                                    )
+
+                                    if state.CB is not None:
+                                        # we converted 8-bit row major to turing/ampere format
+                                        # in the first inference pass
+                                        # we no longer need the row-major weight
+                                        del state.CB
+                                        weight.data = state.CxB
+
+                                    return out
+
+                                return linear
+
+                            module.linear = replace_linear(state)
+
+                        else:
+                            tensor = tensor.to(device)
+
+                    if current_parameter_tensor is not None:
+                        module._parameters[param_name] = tensor
+                    else:
+                        module._buffers[param_name] = tensor
+
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
+        if self.model.gpt_neox.tp_embeddings:
+            outputs = self.model.forward(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=True,
+            )
+
+            # Logits are sharded, so we need to gather them
+            logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
+            torch.distributed.all_gather(
+                logits, outputs.logits, group=self.process_group
+            )
+            logits = torch.cat(logits, dim=2)
+
+            return logits, outputs.past_key_values
+        # While the model itself is sharded, the embeddings might not as they might not be dividable by num-shard
+        else:
+            return super(GPTNeoxSharded, self).forward(
+                input_ids, attention_mask, position_ids, past_key_values
+            )
diff --git a/server/text_generation_server/models/model.py b/server/text_generation_server/models/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfae829d6dc2d029400caa42acd1ba529ae14963
--- /dev/null
+++ b/server/text_generation_server/models/model.py
@@ -0,0 +1,97 @@
+import torch
+
+from abc import ABC, abstractmethod
+from typing import List, Tuple, Optional, TypeVar, Type
+from transformers import PreTrainedTokenizerBase
+
+from text_generation_server.models.types import Batch, GeneratedText
+from text_generation_server.pb.generate_pb2 import InfoResponse
+
+B = TypeVar("B", bound=Batch)
+
+
+class Model(ABC):
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        requires_padding: bool,
+        dtype: torch.dtype,
+        device: torch.device,
+        decode_buffer: int = 3,
+    ):
+        if decode_buffer < 1:
+            raise ValueError("decode_buffer must be >= 1")
+
+        self.tokenizer = tokenizer
+        self.all_special_ids = set(tokenizer.all_special_ids)
+        self.requires_padding = requires_padding
+        self.dtype = dtype
+        self.device = device
+        self.decode_buffer = decode_buffer
+
+    @property
+    def info(self) -> InfoResponse:
+        return InfoResponse(
+            requires_padding=self.requires_padding,
+            dtype=str(self.dtype),
+            device_type=self.device.type,
+        )
+
+    @property
+    @abstractmethod
+    def batch_type(self) -> Type[B]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def generate_token(self, batch: B) -> Tuple[List[GeneratedText], Optional[B]]:
+        raise NotImplementedError
+
+    def decode_token(
+        self,
+        all_input_ids: List[int],
+        offset: Optional[int] = None,
+        token_offset: Optional[int] = None,
+    ) -> Tuple[str, Optional[int], Optional[int]]:
+        """Hack to hopefully support generate_stream for the maximum number of tokenizers"""
+        if all_input_ids[-1] in self.all_special_ids:
+            return (
+                self.tokenizer.decode(all_input_ids[-1], skip_special_tokens=False),
+                None,
+                None,
+            )
+
+        if token_offset is None:
+            token_offset = len(all_input_ids) - self.decode_buffer
+            # left token buffer
+            if self.decode_buffer > 1:
+                # Decode token_offset token minus last one and token_offset tokens
+                raw_texts = self.tokenizer.batch_decode(
+                    [all_input_ids[token_offset:-1], all_input_ids[token_offset:]],
+                    skip_special_tokens=False,
+                )
+
+                # default offset is only the last token
+                offset = len(raw_texts[0])
+                sequence_text = raw_texts[1]
+            else:
+                # Only decode the last token without using a token buffer
+                sequence_text = self.tokenizer.decode(
+                    all_input_ids[-1], skip_special_tokens=False
+                )
+                # no offset in this case
+                offset = 0
+        else:
+            assert offset is not None
+            sequence_text = self.tokenizer.decode(
+                all_input_ids[token_offset:],
+                skip_special_tokens=False,
+            )
+
+        # get text
+        token_text = sequence_text[offset:]
+
+        # if text is utf-8
+        if token_text and token_text[-1] != "�":
+            return token_text, None, None
+        else:
+            return "", offset, token_offset
diff --git a/server/text_generation_server/models/opt.py b/server/text_generation_server/models/opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a21186f51ddc34e365a1e127d8e5a6f3e524e37
--- /dev/null
+++ b/server/text_generation_server/models/opt.py
@@ -0,0 +1,228 @@
+import torch
+import torch.distributed
+
+from typing import List, Optional, Tuple
+
+from accelerate import init_empty_weights
+from safetensors import safe_open
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+)
+from transformers.models.opt.parallel_layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+)
+
+from text_generation_server.models import CausalLM
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+)
+
+HAS_BITS_AND_BYTES = True
+try:
+    import bitsandbytes as bnb
+    from bitsandbytes.nn import Int8Params
+except Exception as e:
+    HAS_BITS_AND_BYTES = False
+
+
+class OPT(CausalLM):
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
+        """Overwrite forward to ignore position_ids"""
+
+        # Model Forward
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+        return outputs.logits, outputs.past_key_values
+
+
+class OPTSharded(OPT):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+
+        config = AutoConfig.from_pretrained(
+            model_id, revision=revision, tp_parallel=True
+        )
+        tokenizer.pad_token_id = config.pad_token_id
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = AutoModelForCausalLM.from_config(config)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+        )
+        self.model = model.eval()
+        torch.distributed.barrier(group=self.process_group)
+        super(CausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+    ):
+        parameters = dict(model.named_parameters())
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for name in f.keys():
+                    if name == "lm_head.weight":
+                        continue
+
+                    module_name, param_name = name.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+                    current_tensor = parameters[name]
+
+                    slice_ = f.get_slice(name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif isinstance(module, TensorParallelRowLinear):
+                        if param_name == "weight":
+                            size = slice_.get_shape()[1]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = slice_[:, start:stop]
+                        else:
+                            tensor = slice_[:]
+                            # XXX: Hack for Rowlinear to add the bias only once.
+                            if rank != 0:
+                                tensor = torch.zeros_like(tensor)
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    else:
+                        tensor = slice_[:]
+
+                    if current_tensor.shape != tensor.shape:
+                        raise ValueError(
+                            f"Name {name} -- Current {current_tensor.shape} and got {tensor.shape}"
+                        )
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    if quantize:
+                        if not HAS_BITS_AND_BYTES:
+                            raise ImportError(
+                                "bitsandbytes is not available on your machine either because it is not installed "
+                                "or you don't have a GPU.\n"
+                                "You can install it with `pip install bitsandbytes`."
+                            )
+
+                        if (
+                            type(module)
+                            in [TensorParallelRowLinear, TensorParallelColumnLinear]
+                            and param_name == "weight"
+                        ):
+                            tensor = Int8Params(
+                                tensor,
+                                has_fp16_weights=False,
+                                requires_grad=False,
+                            ).to(device)
+                            state = bnb.MatmulLtState()
+                            state.threshold = 6.0
+                            state.has_fp16_weights = False
+                            state.memory_efficient_backward = False
+                            state.use_pool = True
+                            state.CB = tensor.CB
+                            state.SCB = tensor.SCB
+                            tensor.CB = None
+                            tensor.SCB = None
+
+                            def replace_linear(state):
+                                def linear(input, weight, bias):
+                                    out = bnb.matmul(
+                                        input,
+                                        weight,
+                                        state=state,
+                                        threshold=state.threshold,
+                                        bias=bias,
+                                    )
+
+                                    if state.CB is not None:
+                                        # we converted 8-bit row major to turing/ampere format
+                                        # in the first inference pass
+                                        # we no longer need the row-major weight
+                                        del state.CB
+                                        weight.data = state.CxB
+
+                                    return out
+
+                                return linear
+
+                            module.linear = replace_linear(state)
+
+                        else:
+                            tensor = tensor.to(device)
+
+                    module._parameters[param_name] = tensor
+                    if name == "model.decoder.embed_tokens.weight":
+                        model.lm_head._parameters["weight"] = tensor
+
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+
+        # Logits are sharded, so we need to gather them
+        logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
+        torch.distributed.all_gather(logits, outputs.logits, group=self.process_group)
+        logits = torch.cat(logits, dim=2)
+
+        return logits, outputs.past_key_values
diff --git a/server/text_generation_server/models/santacoder.py b/server/text_generation_server/models/santacoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..796c33e31bfbcf6a24b3b51b26741a57c5c7868b
--- /dev/null
+++ b/server/text_generation_server/models/santacoder.py
@@ -0,0 +1,68 @@
+import torch
+import torch.distributed
+
+from typing import Optional, List
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+from text_generation_server.models import CausalLM
+
+FIM_PREFIX = "<fim-prefix>"
+FIM_MIDDLE = "<fim-middle>"
+FIM_SUFFIX = "<fim-suffix>"
+FIM_PAD = "<fim-pad>"
+EOD = "<|endoftext|>"
+
+
+class SantaCoder(CausalLM):
+    def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            if quantize:
+                raise ValueError("quantization is not available on CPU")
+
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+        tokenizer.add_special_tokens(
+            {
+                "additional_special_tokens": [
+                    EOD,
+                    FIM_PREFIX,
+                    FIM_MIDDLE,
+                    FIM_SUFFIX,
+                    FIM_PAD,
+                ],
+                "pad_token": EOD,
+            }
+        )
+
+        self.model = (
+            AutoModelForCausalLM.from_pretrained(
+                model_id,
+                revision=revision,
+                torch_dtype=dtype,
+                load_in_8bit=quantize,
+                trust_remote_code=True,  # required
+            )
+            .to(device)
+            .eval()
+        )
+
+        super(CausalLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+            decode_buffer=1,
+        )
+
+    def decode(self, generated_ids: List[int]) -> str:
+        # Do not skip special tokens as they are used for custom parsing rules of the generated text
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=False, cleanup_tokenization_spaces=False
+        )
diff --git a/server/text_generation_server/models/seq2seq_lm.py b/server/text_generation_server/models/seq2seq_lm.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ac5ed3c2433258bdcc594727f18c39041199952
--- /dev/null
+++ b/server/text_generation_server/models/seq2seq_lm.py
@@ -0,0 +1,719 @@
+import torch
+
+from dataclasses import dataclass
+from opentelemetry import trace
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, PreTrainedTokenizerBase
+from typing import Optional, Tuple, List, Type, Dict
+
+from text_generation_server.models import Model
+from text_generation_server.models.types import (
+    GeneratedText,
+    Batch,
+    Generation,
+    PrefillTokens,
+)
+from text_generation_server.pb import generate_pb2
+from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
+
+tracer = trace.get_tracer(__name__)
+
+
+@dataclass
+class Seq2SeqLMBatch(Batch):
+    batch_id: int
+    requests: List[generate_pb2.Request]
+    requests_idx_mapping: Dict[int, int]
+
+    # Encoder values
+    input_ids: Optional[torch.Tensor]
+    attention_mask: torch.Tensor
+
+    # Decoder values
+    decoder_input_ids: torch.Tensor
+    decoder_attention_mask: Optional[torch.Tensor]
+    encoder_last_hidden_state: Optional[torch.Tensor]
+
+    # All tokens
+    all_decoder_input_ids: List[torch.Tensor]
+
+    # Seq2SeqLM keeps track of both encoder and decoder attention keys and values
+    past_key_values: Optional[List[Tuple]]
+
+    # Lengths of all generations present in the batch
+    input_lengths: List[int]
+    decoder_input_lengths: List[int]
+    offsets: List[Optional[int]]
+    token_offsets: List[Optional[int]]
+
+    # Generation helpers
+    next_token_choosers: List[NextTokenChooser]
+    stopping_criterias: List[StoppingCriteria]
+
+    # Metadata used for padding
+    max_input_length: int
+    max_decoder_input_length: int
+    padding_right_offset: int
+
+    # Maximum number of tokens this batch will grow to
+    max_tokens: int
+
+    def to_pb(self) -> generate_pb2.Batch:
+        """Convert a Seq2SeqLMBatch to a text_generation_server.v1.Batch protobuf"""
+        return generate_pb2.Batch(
+            id=self.batch_id,
+            requests=self.requests,
+            size=len(self),
+            max_tokens=self.max_tokens,
+        )
+
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        device: torch.device,
+    ) -> "Seq2SeqLMBatch":
+        """Convert a text_generation_server.v1.Batch protobuf to a Seq2SeqLMBatch"""
+        inputs = []
+        next_token_choosers = []
+        stopping_criterias = []
+
+        decoder_input_lengths = []
+        offsets = []
+        token_offsets = []
+        requests_idx_mapping = {}
+
+        # Parse batch
+        max_truncation = 0
+        padding_right_offset = 0
+        max_decode_tokens = 0
+        for i, r in enumerate(pb.requests):
+            inputs.append(r.inputs)
+            requests_idx_mapping[r.id] = i
+            decoder_input_lengths.append(1)
+            offsets.append(None)
+            token_offsets.append(None)
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
+            stopping_criteria = StoppingCriteria.from_pb(
+                r.stopping_parameters, tokenizer
+            )
+            stopping_criterias.append(stopping_criteria)
+            max_truncation = max(max_truncation, r.truncate)
+            max_decode_tokens += stopping_criteria.max_new_tokens
+            padding_right_offset = max(
+                padding_right_offset, stopping_criteria.max_new_tokens
+            )
+
+        # Tokenize batch
+        tokenized_inputs = tokenizer(
+            inputs,
+            return_tensors="pt",
+            padding=True,
+            return_token_type_ids=False,
+            truncation=True,
+            max_length=max_truncation,
+        ).to(device)
+
+        input_lengths = tokenized_inputs["attention_mask"].sum(1)
+        max_input_length = input_lengths.max()
+
+        # Decoder sequence only contains the bos_token
+        decoder_input_ids = (
+            torch.tensor(tokenizer.bos_token_id, device=device)
+            .repeat(len(pb.requests))
+            .view(-1, 1)
+        )
+        all_decoder_input_ids = decoder_input_ids.view(-1).split(1)
+
+        max_tokens = len(inputs) * max_input_length + max_decode_tokens
+
+        return cls(
+            batch_id=pb.id,
+            requests=pb.requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=tokenized_inputs["input_ids"],
+            attention_mask=tokenized_inputs["attention_mask"],
+            decoder_input_ids=decoder_input_ids,
+            all_decoder_input_ids=list(all_decoder_input_ids),
+            decoder_attention_mask=None,
+            encoder_last_hidden_state=None,
+            past_key_values=None,
+            input_lengths=input_lengths.tolist(),
+            decoder_input_lengths=decoder_input_lengths,
+            offsets=offsets,
+            token_offsets=token_offsets,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            max_input_length=max_input_length.item(),
+            max_decoder_input_length=1,
+            padding_right_offset=padding_right_offset,
+            max_tokens=max_tokens,
+        )
+
+    @tracer.start_as_current_span("filter")
+    def filter(
+        self, requests: List[generate_pb2.Request]
+    ) -> Optional["Seq2SeqLMBatch"]:
+        if len(requests) == 0:
+            raise ValueError("Batch must have at least one request")
+        if len(requests) == len(self):
+            return self
+
+        keep_indices = []
+
+        # New values after filtering
+        requests_idx_mapping = {}
+        input_lengths = []
+        decoder_input_lengths = []
+        offsets = []
+        token_offsets = []
+
+        all_decoder_input_ids = []
+
+        next_token_choosers = []
+        stopping_criterias = []
+
+        max_input_length = 0
+        max_decoder_input_length = 0
+        padding_right_offset = 0
+
+        total_remaining_decode_tokens = 0
+
+        for i, r in enumerate(requests):
+            idx = self.requests_idx_mapping[r.id]
+            requests_idx_mapping[r.id] = i
+            keep_indices.append(idx)
+
+            offsets.append(self.offsets[idx])
+            token_offsets.append(self.token_offsets[idx])
+
+            all_decoder_input_ids.append(self.all_decoder_input_ids[idx])
+
+            request_input_length = self.input_lengths[idx]
+            input_lengths.append(request_input_length)
+            max_input_length = max(max_input_length, request_input_length)
+
+            request_decoder_input_length = self.decoder_input_lengths[idx]
+            decoder_input_lengths.append(request_decoder_input_length)
+            max_decoder_input_length = max(
+                max_decoder_input_length, request_decoder_input_length
+            )
+
+            next_token_choosers.append(self.next_token_choosers[idx])
+            stopping_criteria = self.stopping_criterias[idx]
+            stopping_criterias.append(stopping_criteria)
+            remaining_decode_tokens = (
+                stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
+            )
+            total_remaining_decode_tokens += remaining_decode_tokens
+            padding_right_offset = max(padding_right_offset, remaining_decode_tokens)
+
+        # Apply indices to input_ids, attention mask, past key values and other items that need to be cached
+        self.decoder_input_ids = self.decoder_input_ids[keep_indices]
+        self.attention_mask = self.attention_mask[keep_indices, -max_input_length:]
+        if self.decoder_attention_mask is not None:
+            self.decoder_attention_mask = self.decoder_attention_mask[
+                keep_indices,
+                -(self.padding_right_offset + max_decoder_input_length) : (
+                    self.decoder_attention_mask.shape[1] - self.padding_right_offset
+                )
+                + padding_right_offset,
+            ]
+
+        self.encoder_last_hidden_state = self.encoder_last_hidden_state[
+            keep_indices, -max_input_length:
+        ]
+
+        # Ensure that past_key_values tensors can be updated in-place
+        if type(self.past_key_values[0]) == tuple:
+            self.past_key_values = [
+                [t for t in layer] for layer in self.past_key_values
+            ]
+
+        decoder_past_seq_len = max_decoder_input_length - 1
+        for layer in self.past_key_values:
+            layer[0] = layer[0][keep_indices, :, -decoder_past_seq_len:]
+            layer[1] = layer[1][keep_indices, :, -decoder_past_seq_len:]
+            layer[2] = layer[2][keep_indices, :, -max_input_length:]
+            layer[3] = layer[3][keep_indices, :, -max_input_length:]
+
+        max_tokens = (
+            len(requests) * (max_input_length + max_decoder_input_length)
+            + remaining_decode_tokens
+        )
+
+        self.requests = requests
+        self.requests_idx_mapping = requests_idx_mapping
+        self.input_ids = None
+        self.all_decoder_input_ids = all_decoder_input_ids
+        self.input_lengths = input_lengths
+        self.decoder_input_lengths = decoder_input_lengths
+        self.offsets = offsets
+        self.token_offsets = token_offsets
+        self.next_token_choosers = next_token_choosers
+        self.stopping_criterias = stopping_criterias
+        self.max_input_length = max_input_length
+        self.max_decoder_input_length = max_decoder_input_length
+        self.padding_right_offset = padding_right_offset
+        self.max_tokens = max_tokens
+
+        return self
+
+    @classmethod
+    @tracer.start_as_current_span("concatenate")
+    def concatenate(cls, batches: List["Seq2SeqLMBatch"]) -> "Seq2SeqLMBatch":
+        """Concatenate multiple batches together by padding internal torch tensors"""
+
+        # Used for padding
+        total_batch_size = 0
+        max_input_length = 0
+        max_decoder_input_length = 0
+        padding_right_offset = 0
+        for batch in batches:
+            total_batch_size += len(batch)
+            max_input_length = max(max_input_length, batch.max_input_length)
+            max_decoder_input_length = max(
+                max_decoder_input_length, batch.max_decoder_input_length
+            )
+            padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
+
+        # Batch attributes
+        requests = []
+        requests_idx_mapping = {}
+        all_decoder_input_ids = []
+        input_lengths = []
+        decoder_input_lengths = []
+        offsets = []
+        token_offsets = []
+        next_token_choosers = []
+        stopping_criterias = []
+        max_tokens = 0
+
+        # Batch tensors
+        attention_mask = None
+        decoder_input_ids = None
+        decoder_attention_mask = None
+        encoder_last_hidden_state = None
+        past_key_values = []
+
+        # Used for slicing correctly inside the tensors
+        # Equivalent to a cumsum on batch sizes
+        start_index = 0
+
+        for i, batch in enumerate(batches):
+            # Extend all list attributes
+            requests.extend(batch.requests)
+            all_decoder_input_ids.extend(batch.all_decoder_input_ids)
+            input_lengths.extend(batch.input_lengths)
+            decoder_input_lengths.extend(batch.decoder_input_lengths)
+            offsets.extend(batch.offsets)
+            token_offsets.extend(batch.token_offsets)
+            next_token_choosers.extend(batch.next_token_choosers)
+            stopping_criterias.extend(batch.stopping_criterias)
+
+            if i == 0:
+                requests_idx_mapping = batch.requests_idx_mapping
+            else:
+                # We need to offset the mapping for each batch by the cumulative batch size
+                for k, v in batch.requests_idx_mapping.items():
+                    requests_idx_mapping[k] = v + start_index
+
+            # Slicing end index for this batch
+            end_index = start_index + len(batch)
+
+            # We only concatenate batches that did at least one step
+            if batch.encoder_last_hidden_state is None:
+                raise ValueError("Batch encoder_last_hidden_state cannot be None")
+
+            # Create padded tensor
+            if attention_mask is None:
+                attention_mask = batch.attention_mask.new_zeros(
+                    (total_batch_size, max_input_length),
+                )
+            # Copy to correct indices
+            attention_mask[
+                start_index:end_index, -batch.max_input_length :
+            ] = batch.attention_mask[:, -batch.max_input_length :]
+
+            # Create padded tensor
+            if decoder_input_ids is None:
+                decoder_input_ids = batch.decoder_input_ids.new_zeros(
+                    (total_batch_size, 1),
+                )
+            # Copy to correct indices
+            decoder_input_ids[start_index:end_index] = batch.decoder_input_ids
+
+            # Create padded tensor
+            if decoder_attention_mask is None:
+                # As decoder_attention_mask might not exist, we use `batch.attention_mask` for device here
+                decoder_attention_mask = batch.attention_mask.new_zeros(
+                    (total_batch_size, max_decoder_input_length + padding_right_offset),
+                )
+            # If the decoder mask does not exist yet, all generations started at the same time and we never concatenated
+            # this batch. All generations are of length `batch.max_decoder_input_length`.
+            left_offset = max_decoder_input_length - batch.max_decoder_input_length
+            if batch.decoder_attention_mask is None:
+                decoder_attention_mask[
+                    start_index:end_index,
+                    left_offset:-padding_right_offset,
+                ] = 1
+            # If it exists, we need to index
+            else:
+                batch_left_offset = (
+                    batch.decoder_attention_mask.shape[1]
+                    - batch.max_decoder_input_length
+                    - batch.padding_right_offset
+                )
+                decoder_attention_mask[
+                    start_index:end_index,
+                    left_offset:-padding_right_offset,
+                ] = batch.decoder_attention_mask[
+                    :,
+                    batch_left_offset : -batch.padding_right_offset,
+                ]
+
+            # Create padded tensor
+            if encoder_last_hidden_state is None:
+                encoder_last_hidden_state = batch.encoder_last_hidden_state.new_zeros(
+                    (
+                        total_batch_size,
+                        max_input_length,
+                        batch.encoder_last_hidden_state.shape[-1],
+                    ),
+                )
+
+            # Copy to correct indices
+            encoder_last_hidden_state[
+                start_index:end_index, -batch.max_input_length :, :
+            ] = batch.encoder_last_hidden_state[:, -batch.max_input_length :, :]
+            batch.encoder_last_hidden_state = None
+
+            # Ensure that we can update tensors in-place
+            if type(batch.past_key_values[0]) == tuple:
+                batch.past_key_values = [
+                    [t for t in layer] for layer in batch.past_key_values
+                ]
+
+            # Add eventual padding tokens that were added while concatenating
+            max_tokens += batch.max_tokens + (
+                max_input_length
+                - batch.max_input_length
+                + max_decoder_input_length
+                - batch.max_decoder_input_length
+            ) * len(batch)
+
+            start_index = end_index
+
+        # Determine shapes for new past kv tensors
+        first_past_kvs = batches[0].past_key_values
+        _, num_heads, _, head_dim = first_past_kvs[0][0].shape
+
+        padded_dec_t_shape = (
+            total_batch_size,
+            num_heads,
+            (max_decoder_input_length - 1),
+            head_dim,
+        )
+
+        padded_enc_t_shape = (
+            total_batch_size,
+            num_heads,
+            max_input_length,
+            head_dim,
+        )
+
+        # Iterate over attention layers
+        for j in range(len(first_past_kvs)):
+            past_key_values.append([])
+
+            # Decoder past
+            for k in range(0, 2):
+                # Initialize tensors
+                padded_past_values = first_past_kvs[j][k].new_zeros(padded_dec_t_shape)
+                past_key_values[j].append(padded_past_values)
+
+                start_index = 0
+                for batch in batches:
+                    t = batch.past_key_values[j][k]
+                    # Clear reference to the original tensor
+                    batch.past_key_values[j][k] = None
+                    # Slicing end index for this batch
+                    end_index = start_index + len(batch)
+                    # We slice the past keys and values to remove the padding from previous batches
+                    past_seq_len = batch.max_decoder_input_length - 1
+                    padded_past_values[start_index:end_index, :, -past_seq_len:, :] = t[
+                        :, :, -past_seq_len:, :
+                    ]
+                    del t
+
+                    start_index = end_index
+
+            # Encoder past
+            for k in range(2, 4):
+                # Initialize tensors
+                padded_past_values = first_past_kvs[j][k].new_zeros(padded_enc_t_shape)
+                past_key_values[j].append(padded_past_values)
+
+                start_index = 0
+                for batch in batches:
+                    t = batch.past_key_values[j][k]
+                    # Clear reference to the original tensor
+                    batch.past_key_values[j][k] = None
+                    # Slicing end index for this batch
+                    end_index = start_index + len(batch)
+                    # We slice the past keys and values to remove the padding from previous batches
+                    padded_past_values[
+                        start_index:end_index, :, -batch.max_input_length :, :
+                    ] = t[:, :, -batch.max_input_length :, :]
+                    del t
+
+                    start_index = end_index
+
+        return cls(
+            batch_id=batches[0].batch_id,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=None,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            all_decoder_input_ids=all_decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_last_hidden_state=encoder_last_hidden_state,
+            past_key_values=past_key_values,
+            input_lengths=input_lengths,
+            decoder_input_lengths=decoder_input_lengths,
+            offsets=offsets,
+            token_offsets=token_offsets,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            max_input_length=max_input_length,
+            max_decoder_input_length=max_decoder_input_length,
+            padding_right_offset=padding_right_offset,
+            max_tokens=max_tokens,
+        )
+
+    def __len__(self):
+        return len(self.requests)
+
+
+class Seq2SeqLM(Model):
+    def __init__(
+        self,
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: bool = False,
+        decode_buffer: int = 3,
+    ):
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            if quantize:
+                raise ValueError("quantization is not available on CPU")
+
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(
+            model_id,
+            revision=revision,
+            torch_dtype=dtype,
+            device_map="auto" if torch.cuda.is_available() else None,
+            load_in_8bit=quantize,
+        ).eval()
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+        tokenizer.bos_token_id = self.model.config.decoder_start_token_id
+
+        super(Seq2SeqLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+            decode_buffer=decode_buffer,
+        )
+
+    @property
+    def batch_type(self) -> Type[Seq2SeqLMBatch]:
+        return Seq2SeqLMBatch
+
+    def decode(self, decoder_ids: List[int]) -> str:
+        return self.tokenizer.decode(
+            decoder_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask,
+        decoder_input_ids,
+        decoder_attention_mask: Optional,
+        encoder_last_hidden_state: Optional,
+        past_key_values: Optional = None,
+    ) -> Tuple[
+        torch.Tensor,
+        torch.Tensor,
+        List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
+    ]:
+        # Model Forward
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_last_hidden_state,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+        return (
+            outputs.logits,
+            outputs.encoder_last_hidden_state,
+            outputs.past_key_values,
+        )
+
+    @tracer.start_as_current_span("generate_token")
+    def generate_token(
+        self, batch: Seq2SeqLMBatch
+    ) -> Tuple[List[Generation], Optional[Seq2SeqLMBatch]]:
+        if batch.decoder_attention_mask is not None:
+            # slice to the correct shape
+            decoder_attention_mask = batch.decoder_attention_mask[
+                :, : -batch.padding_right_offset
+            ]
+        else:
+            decoder_attention_mask = None
+
+        # Wrap `encoder_last_hidden_state` because for some reason, Transformers does a `encoder_last_hidden_state[0]`
+        # internally...
+        if batch.encoder_last_hidden_state is not None:
+            encoder_last_hidden_state = [batch.encoder_last_hidden_state]
+        else:
+            encoder_last_hidden_state = None
+
+        logits, encoder_last_hidden_state, past = self.forward(
+            batch.input_ids,
+            batch.attention_mask,
+            batch.decoder_input_ids,
+            decoder_attention_mask,
+            encoder_last_hidden_state,
+            batch.past_key_values,
+        )
+
+        # Finished requests
+        generations: List[Generation] = []
+        stopped = True
+
+        # Zipped iterator
+        iterator = zip(
+            batch.requests,
+            batch.input_lengths,
+            batch.offsets,
+            batch.token_offsets,
+            batch.decoder_input_lengths,
+            logits,
+            batch.next_token_choosers,
+            batch.stopping_criterias,
+            batch.all_decoder_input_ids,
+        )
+
+        # For each member of the batch
+        for i, (
+            request,
+            input_length,
+            offset,
+            token_offset,
+            decoder_input_length,
+            logits,
+            next_token_chooser,
+            stopping_criteria,
+            all_decoder_input_ids,
+        ) in enumerate(iterator):
+            # Select next token
+            next_token_id, logprobs = next_token_chooser(
+                all_decoder_input_ids.view(1, -1), logits
+            )
+
+            # Append next token to decoder tokens
+            all_decoder_input_ids = torch.cat(
+                [all_decoder_input_ids, next_token_id.squeeze(1)]
+            )
+            new_decoder_input_length = decoder_input_length + 1
+
+            # Generated token
+            next_token_logprob = logprobs[-1, next_token_id]
+            next_token_id_squeezed = next_token_id.squeeze()
+            next_token_text, offset, token_offset = self.decode_token(
+                all_decoder_input_ids, offset, token_offset
+            )
+
+            # Evaluate stopping criteria
+            stop, reason = stopping_criteria(next_token_id, next_token_text)
+
+            if stop:
+                # Slice with decoder_input_length to remove padding
+                # Decode all tokens
+                output_text = self.decode(all_decoder_input_ids[-decoder_input_length:])
+
+                # Get seed
+                if isinstance(next_token_chooser.choice, Sampling):
+                    seed = next_token_chooser.choice.seed
+                else:
+                    seed = None
+
+                generated_text = GeneratedText(
+                    output_text, stopping_criteria.current_tokens, reason, seed
+                )
+            else:
+                # Keep request in the batch
+                generated_text = None
+                stopped = False
+
+            # Prefill
+            if stopping_criteria.current_tokens == 1:
+                prefill_tokens = PrefillTokens(
+                    [self.tokenizer.bos_token_id],
+                    [float("nan")],
+                    [self.tokenizer.bos_token],
+                )
+            else:
+                prefill_tokens = None
+
+            generation = Generation(
+                request.id,
+                prefill_tokens,
+                next_token_id_squeezed,
+                next_token_logprob,
+                next_token_text,
+                next_token_id_squeezed.item() in self.all_special_ids,
+                generated_text,
+            )
+
+            generations.append(generation)
+
+            # Update values
+            batch.decoder_input_ids[i] = next_token_id
+            batch.all_decoder_input_ids[i] = all_decoder_input_ids
+            batch.input_lengths[i] = input_length
+            batch.decoder_input_lengths[i] = new_decoder_input_length
+            batch.offsets[i] = offset
+            batch.token_offsets[i] = token_offset
+            batch.max_input_length = max(batch.max_input_length, input_length)
+            batch.max_decoder_input_length = max(
+                batch.max_decoder_input_length, new_decoder_input_length
+            )
+
+        # We finished all generations in the batch; there is no next batch
+        if stopped:
+            return generations, None
+
+        # We don't need input_ids after the prefill forward
+        batch.input_ids = None
+        batch.encoder_last_hidden_state = encoder_last_hidden_state
+        batch.past_key_values = past
+        # Update decoder_attention_mask as we added a new token to input_ids
+        if batch.decoder_attention_mask is not None:
+            batch.decoder_attention_mask[:, -batch.padding_right_offset] = 1
+        batch.padding_right_offset -= 1
+
+        return generations, batch
diff --git a/server/text_generation_server/models/t5.py b/server/text_generation_server/models/t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..487a5984c989d9ffc966ea187f6649a277fc2efe
--- /dev/null
+++ b/server/text_generation_server/models/t5.py
@@ -0,0 +1,247 @@
+import torch
+import torch.distributed
+
+from typing import List, Optional, Tuple
+
+from accelerate import init_empty_weights
+from safetensors import safe_open
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM,
+    AutoConfig,
+)
+from transformers.models.t5.parallel_layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+)
+
+from text_generation_server.models import Seq2SeqLM
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+)
+
+HAS_BITS_AND_BYTES = True
+try:
+    import bitsandbytes as bnb
+    from bitsandbytes.nn import Int8Params
+except Exception as e:
+    HAS_BITS_AND_BYTES = False
+
+
+class T5Sharded(Seq2SeqLM):
+    def __init__(
+        self, model_id: str, revision: Optional[str] = None, quantize: bool = False
+    ):
+        self.process_group, self.rank, self.world_size = initialize_torch_distributed()
+        self.master = self.rank == 0
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{self.rank}")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left", truncation_side="left"
+        )
+
+        config = AutoConfig.from_pretrained(
+            model_id, revision=revision, tp_parallel=True
+        )
+        tokenizer.bos_token_id = config.decoder_start_token_id
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+
+        with init_empty_weights():
+            model = AutoModelForSeq2SeqLM.from_config(config)
+
+        torch.distributed.barrier(group=self.process_group)
+        self.load_weights(
+            model,
+            filenames,
+            quantize=quantize,
+            device=device,
+            dtype=dtype,
+            rank=self.rank,
+            world_size=self.world_size,
+        )
+        self.model = model.eval()
+        torch.distributed.barrier(group=self.process_group)
+        super(Seq2SeqLM, self).__init__(
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+        )
+
+    @staticmethod
+    def load_weights(
+        model,
+        filenames: List[str],
+        quantize: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        rank: int,
+        world_size: int,
+    ):
+        parameters = dict(model.named_parameters())
+        for file in filenames:
+            with safe_open(
+                file, framework="pt", device=str(device) if not quantize else "cpu"
+            ) as f:
+                for name in f.keys():
+                    module_name, param_name = name.rsplit(".", 1)
+                    module = model.get_submodule(module_name)
+
+                    current_parameter_tensor = parameters.get(name, None)
+
+                    slice_ = f.get_slice(name)
+
+                    if isinstance(module, TensorParallelColumnLinear):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif isinstance(module, TensorParallelRowLinear):
+                        if param_name == "weight":
+                            size = slice_.get_shape()[1]
+                            block_size = size // world_size
+                            start = rank * block_size
+                            stop = (rank + 1) * block_size
+                            tensor = slice_[:, start:stop]
+                        else:
+                            tensor = slice_[:]
+                            # XXX: Hack for Rowlinear to add the bias only once.
+                            if rank != 0:
+                                tensor = torch.zeros_like(tensor)
+                    elif isinstance(module, TensorParallelEmbedding):
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif name == "lm_head.weight":
+                        size = slice_.get_shape()[0]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[start:stop]
+                    elif "relative_attention_bias.weight" in name:
+                        size = slice_.get_shape()[1]
+                        block_size = size // world_size
+                        start = rank * block_size
+                        stop = (rank + 1) * block_size
+                        tensor = slice_[:, start:stop]
+                    else:
+                        try:
+                            tensor = slice_[:]
+                        except:
+                            tensor = f.get_tensor(name)
+
+                    if (
+                        current_parameter_tensor is not None
+                        and current_parameter_tensor.shape != tensor.shape
+                    ):
+                        raise ValueError(
+                            f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
+                        )
+
+                    tensor = tensor.contiguous().to(dtype)
+
+                    if quantize:
+                        if not HAS_BITS_AND_BYTES:
+                            raise ImportError(
+                                "bitsandbytes is not available on your machine either because it is not installed "
+                                "or you don't have a GPU.\n"
+                                "You can install it with `pip install bitsandbytes`."
+                            )
+
+                        if (
+                            type(module)
+                            in [TensorParallelRowLinear, TensorParallelColumnLinear]
+                            and param_name == "weight"
+                        ):
+                            tensor = Int8Params(
+                                tensor,
+                                has_fp16_weights=False,
+                                requires_grad=False,
+                            ).to(device)
+                            state = bnb.MatmulLtState()
+                            state.threshold = 6.0
+                            state.has_fp16_weights = False
+                            state.memory_efficient_backward = False
+                            state.use_pool = True
+                            state.CB = tensor.CB
+                            state.SCB = tensor.SCB
+                            tensor.CB = None
+                            tensor.SCB = None
+
+                            def replace_linear(state):
+                                def linear(input, weight, bias):
+                                    out = bnb.matmul(
+                                        input,
+                                        weight,
+                                        state=state,
+                                        threshold=state.threshold,
+                                        bias=bias,
+                                    )
+
+                                    if state.CB is not None:
+                                        # we converted 8-bit row major to turing/ampere format
+                                        # in the first inference pass
+                                        # we no longer need the row-major weight
+                                        del state.CB
+                                        weight.data = state.CxB
+
+                                    return out
+
+                                return linear
+
+                            module.linear = replace_linear(state)
+
+                        else:
+                            tensor = tensor.to(device)
+
+                    if current_parameter_tensor is not None:
+                        module._parameters[param_name] = tensor
+                    else:
+                        module._buffers[param_name] = tensor
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask,
+        decoder_input_ids,
+        decoder_attention_mask: Optional,
+        encoder_last_hidden_state: Optional,
+        past_key_values: Optional = None,
+    ) -> Tuple[
+        torch.Tensor,
+        torch.Tensor,
+        List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
+    ]:
+        # Model Forward
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_last_hidden_state,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+
+        # Logits are sharded, so we need to gather them
+        logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
+        torch.distributed.all_gather(logits, outputs.logits, group=self.process_group)
+        logits = torch.cat(logits, dim=2)
+
+        return (
+            logits,
+            outputs.encoder_last_hidden_state,
+            outputs.past_key_values,
+        )
diff --git a/server/text_generation_server/models/types.py b/server/text_generation_server/models/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a5b82f7fb98084fd2563ffc3a46c88fd4170620
--- /dev/null
+++ b/server/text_generation_server/models/types.py
@@ -0,0 +1,96 @@
+import torch
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import List, Optional
+
+from transformers import PreTrainedTokenizerBase
+
+from text_generation_server.pb import generate_pb2
+from text_generation_server.pb.generate_pb2 import FinishReason
+
+
+class Batch(ABC):
+    @abstractmethod
+    def to_pb(self) -> generate_pb2.Batch:
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        device: torch.device,
+    ) -> "Batch":
+        raise NotImplementedError
+
+    @abstractmethod
+    def filter(self, requests: List[generate_pb2.Request]) -> "Batch":
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def concatenate(cls, batches: List["Batch"]) -> "Batch":
+        raise NotImplementedError
+
+    @abstractmethod
+    def __len__(self):
+        raise NotImplementedError
+
+
+@dataclass
+class GeneratedText:
+    text: str
+    generated_tokens: int
+    finish_reason: FinishReason
+    seed: Optional[int]
+
+    def to_pb(self) -> generate_pb2.GeneratedText:
+        return generate_pb2.GeneratedText(
+            text=self.text,
+            generated_tokens=self.generated_tokens,
+            finish_reason=self.finish_reason,
+            seed=self.seed,
+        )
+
+
+@dataclass
+class PrefillTokens:
+    token_ids: List[int]
+    logprobs: List[float]
+    texts: List[str]
+
+    def to_pb(self) -> generate_pb2.PrefillTokens:
+        return generate_pb2.PrefillTokens(
+            ids=self.token_ids, logprobs=self.logprobs, texts=self.texts
+        )
+
+    def __len__(self):
+        return len(self.token_ids)
+
+
+@dataclass
+class Generation:
+    request_id: int
+    prefill_tokens: Optional[PrefillTokens]
+    token_id: int
+    token_logprob: float
+    token_text: str
+    token_is_special: bool
+    generated_text: Optional[GeneratedText]
+
+    def to_pb(self) -> generate_pb2.Generation:
+        return generate_pb2.Generation(
+            request_id=self.request_id,
+            prefill_tokens=self.prefill_tokens.to_pb()
+            if self.prefill_tokens is not None
+            else None,
+            token_id=self.token_id,
+            token_logprob=self.token_logprob,
+            token_text=self.token_text,
+            token_is_special=self.token_is_special,
+            generated_text=self.generated_text.to_pb()
+            if self.generated_text is not None
+            else None,
+        )
diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py
new file mode 100644
index 0000000000000000000000000000000000000000..70f08ed70398162210562cd93396eef6dbb45d0d
--- /dev/null
+++ b/server/text_generation_server/server.py
@@ -0,0 +1,155 @@
+import asyncio
+import os
+import torch
+
+from grpc import aio
+from loguru import logger
+
+from grpc_reflection.v1alpha import reflection
+from pathlib import Path
+from typing import List, Optional
+
+from text_generation_server.cache import Cache
+from text_generation_server.interceptor import ExceptionInterceptor
+from text_generation_server.models import Model, get_model
+from text_generation_server.pb import generate_pb2_grpc, generate_pb2
+from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
+
+
+class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
+    def __init__(self, model: Model, cache: Cache, server_urls: List[str]):
+        self.cache = cache
+        self.model = model
+        self.server_urls = server_urls
+        # For some reason, inference_mode does not work well with GLOO which we use on CPU
+        if model.device.type == "cuda":
+            # Force inference mode for the lifetime of TextGenerationService
+            self._inference_mode_raii_guard = torch._C._InferenceMode(True)
+
+    async def Info(self, request, context):
+        return self.model.info
+
+    async def Health(self, request, context):
+        if self.model.device.type == "cuda":
+            torch.zeros((2, 2)).cuda()
+        return generate_pb2.HealthResponse()
+
+    async def ServiceDiscovery(self, request, context):
+        return generate_pb2.ServiceDiscoveryResponse(urls=self.server_urls)
+
+    async def ClearCache(self, request, context):
+        if request.HasField("id"):
+            self.cache.delete(request.id)
+        else:
+            self.cache.clear()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return generate_pb2.ClearCacheResponse()
+
+    async def FilterBatch(self, request, context):
+        batch = self.cache.pop(request.batch_id)
+        if batch is None:
+            raise ValueError(f"Batch ID {request.batch_id} not found in cache.")
+        filtered_batch = batch.filter(request.keep_requests)
+        self.cache.set(filtered_batch)
+
+        return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
+
+    async def Prefill(self, request, context):
+        batch = self.model.batch_type.from_pb(
+            request.batch, self.model.tokenizer, self.model.device
+        )
+
+        generations, next_batch = self.model.generate_token(batch)
+        self.cache.set(next_batch)
+
+        return generate_pb2.PrefillResponse(
+            generations=[generation.to_pb() for generation in generations],
+            batch=next_batch.to_pb() if next_batch else None,
+        )
+
+    async def Decode(self, request, context):
+        if len(request.batches) == 0:
+            raise ValueError("Must provide at least one batch")
+
+        batches = []
+        for batch_pb in request.batches:
+            batch = self.cache.pop(batch_pb.id)
+            if batch is None:
+                raise ValueError(f"Batch ID {batch_pb.id} not found in cache.")
+            batches.append(batch)
+
+        if len(batches) == 0:
+            raise ValueError("All batches are empty")
+
+        if len(batches) > 1:
+            batch = self.model.batch_type.concatenate(batches)
+        else:
+            batch = batches[0]
+
+        generations, next_batch = self.model.generate_token(batch)
+        self.cache.set(next_batch)
+
+        return generate_pb2.DecodeResponse(
+            generations=[generation.to_pb() for generation in generations],
+            batch=next_batch.to_pb() if next_batch else None,
+        )
+
+
+def serve(
+    model_id: str,
+    revision: Optional[str],
+    sharded: bool,
+    quantize: bool,
+    uds_path: Path,
+):
+    async def serve_inner(
+        model_id: str,
+        revision: Optional[str],
+        sharded: bool = False,
+        quantize: bool = False,
+    ):
+        unix_socket_template = "unix://{}-{}"
+        if sharded:
+            server_urls = [
+                unix_socket_template.format(uds_path, rank)
+                for rank in range(int(os.environ["WORLD_SIZE"]))
+            ]
+            local_url = server_urls[int(os.environ["RANK"])]
+        else:
+            local_url = unix_socket_template.format(uds_path, 0)
+            server_urls = [local_url]
+
+        try:
+            model = get_model(model_id, revision, sharded, quantize)
+        except Exception:
+            logger.exception("Error when initializing model")
+            raise
+
+        server = aio.server(
+            interceptors=[
+                ExceptionInterceptor(),
+                UDSOpenTelemetryAioServerInterceptor(),
+            ]
+        )
+        generate_pb2_grpc.add_TextGenerationServiceServicer_to_server(
+            TextGenerationService(model, Cache(), server_urls), server
+        )
+        SERVICE_NAMES = (
+            generate_pb2.DESCRIPTOR.services_by_name["TextGenerationService"].full_name,
+            reflection.SERVICE_NAME,
+        )
+        reflection.enable_server_reflection(SERVICE_NAMES, server)
+        server.add_insecure_port(local_url)
+
+        await server.start()
+
+        logger.info("Server started at {}".format(local_url))
+
+        try:
+            await server.wait_for_termination()
+        except KeyboardInterrupt:
+            logger.info("Signal received. Shutting down")
+            await server.stop(0)
+
+    asyncio.run(serve_inner(model_id, revision, sharded, quantize))
diff --git a/server/text_generation_server/tracing.py b/server/text_generation_server/tracing.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf03c379936bd8d7c9cf9e4caa232781f46a0fbd
--- /dev/null
+++ b/server/text_generation_server/tracing.py
@@ -0,0 +1,65 @@
+import grpc
+
+from opentelemetry import trace
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+from opentelemetry.instrumentation.grpc._aio_server import (
+    OpenTelemetryAioServerInterceptor,
+)
+from opentelemetry.semconv.trace import SpanAttributes
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import (
+    BatchSpanProcessor,
+)
+
+
+class UDSOpenTelemetryAioServerInterceptor(OpenTelemetryAioServerInterceptor):
+    def __init__(self):
+        super().__init__(trace.get_tracer(__name__))
+
+    def _start_span(self, handler_call_details, context, set_status_on_exception=False):
+        """
+        Rewrite _start_span method to support Unix Domain Socket gRPC contexts
+        """
+
+        # standard attributes
+        attributes = {
+            SpanAttributes.RPC_SYSTEM: "grpc",
+            SpanAttributes.RPC_GRPC_STATUS_CODE: grpc.StatusCode.OK.value[0],
+        }
+
+        # if we have details about the call, split into service and method
+        if handler_call_details.method:
+            service, method = handler_call_details.method.lstrip("/").split("/", 1)
+            attributes.update(
+                {
+                    SpanAttributes.RPC_METHOD: method,
+                    SpanAttributes.RPC_SERVICE: service,
+                }
+            )
+
+        # add some attributes from the metadata
+        metadata = dict(context.invocation_metadata())
+        if "user-agent" in metadata:
+            attributes["rpc.user_agent"] = metadata["user-agent"]
+
+        # We use gRPC over a UNIX socket
+        attributes.update({SpanAttributes.NET_TRANSPORT: "unix"})
+
+        return self._tracer.start_as_current_span(
+            name=handler_call_details.method,
+            kind=trace.SpanKind.SERVER,
+            attributes=attributes,
+            set_status_on_exception=set_status_on_exception,
+        )
+
+
+def setup_tracing(shard: int, otlp_endpoint: str):
+    resource = Resource.create(
+        attributes={"service.name": f"text-generation-inference.server-{shard}"}
+    )
+    span_exporter = OTLPSpanExporter(endpoint=otlp_endpoint, insecure=True)
+    span_processor = BatchSpanProcessor(span_exporter)
+
+    trace.set_tracer_provider(TracerProvider(resource=resource))
+    trace.get_tracer_provider().add_span_processor(span_processor)
diff --git a/server/text_generation_server/utils/__init__.py b/server/text_generation_server/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..50d64518ec6d4bbb5b3fd70d1d1b77eedb439258
--- /dev/null
+++ b/server/text_generation_server/utils/__init__.py
@@ -0,0 +1,36 @@
+from text_generation_server.utils.convert import convert_file, convert_files
+from text_generation_server.utils.dist import initialize_torch_distributed
+from text_generation_server.utils.hub import (
+    weight_files,
+    weight_hub_files,
+    download_weights,
+    EntryNotFoundError,
+    LocalEntryNotFoundError,
+    RevisionNotFoundError,
+)
+from text_generation_server.utils.tokens import (
+    Greedy,
+    NextTokenChooser,
+    Sampling,
+    StoppingCriteria,
+    StopSequenceCriteria,
+    FinishReason,
+)
+
+__all__ = [
+    "convert_file",
+    "convert_files",
+    "initialize_torch_distributed",
+    "weight_files",
+    "weight_hub_files",
+    "download_weights",
+    "EntryNotFoundError",
+    "LocalEntryNotFoundError",
+    "RevisionNotFoundError",
+    "Greedy",
+    "NextTokenChooser",
+    "Sampling",
+    "StoppingCriteria",
+    "StopSequenceCriteria",
+    "FinishReason",
+]
diff --git a/server/text_generation_server/utils/convert.py b/server/text_generation_server/utils/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..437e2308735eb39c137c4148169528d694c821ba
--- /dev/null
+++ b/server/text_generation_server/utils/convert.py
@@ -0,0 +1,94 @@
+import concurrent
+import time
+import torch
+
+from concurrent.futures import ThreadPoolExecutor
+from collections import defaultdict
+from datetime import timedelta
+from loguru import logger
+from pathlib import Path
+from safetensors.torch import load_file, save_file
+from typing import Dict, List
+
+
+def check_file_size(source_file: Path, target_file: Path):
+    """
+    Check that two files are close in size
+    """
+    source_file_size = source_file.stat().st_size
+    target_file_size = target_file.stat().st_size
+
+    if (source_file_size - target_file_size) / source_file_size > 0.01:
+        raise RuntimeError(
+            f"""The file size different is more than 1%:
+         - {source_file}: {source_file_size}
+         - {target_file}: {target_file_size}
+         """
+        )
+
+
+def remove_shared_pointers(tensors: Dict[str, torch.Tensor]):
+    """
+    For a Dict of tensors, check if two or more tensors point to the same underlying memory and
+    remove them
+    """
+    ptrs = defaultdict(list)
+    for k, v in tensors.items():
+        ptrs[v.data_ptr()].append(k)
+
+    # Iterate over all found memory addresses
+    for ptr, names in ptrs.items():
+        if len(names) > 1:
+            # Multiple tensors are point to the same memory
+            # Only keep the first tensor
+            for name in names[1:]:
+                tensors.pop(name)
+
+
+def convert_file(pt_file: Path, st_file: Path):
+    """
+    Convert a pytorch file to a safetensors file
+    """
+    logger.info(f"Convert {pt_file} to {st_file}.")
+
+    pt_state = torch.load(pt_file, map_location="cpu")
+    if "state_dict" in pt_state:
+        pt_state = pt_state["state_dict"]
+
+    remove_shared_pointers(pt_state)
+
+    # Tensors need to be contiguous
+    pt_state = {k: v.contiguous() for k, v in pt_state.items()}
+
+    st_file.parent.mkdir(parents=True, exist_ok=True)
+    save_file(pt_state, str(st_file), metadata={"format": "pt"})
+
+    # Check that both files are close in size
+    check_file_size(pt_file, st_file)
+
+    # Load safetensors state
+    st_state = load_file(str(st_file))
+    for k in st_state:
+        pt_tensor = pt_state[k]
+        st_tensor = st_state[k]
+        if not torch.equal(pt_tensor, st_tensor):
+            raise RuntimeError(f"The output tensors do not match for key {k}")
+
+
+def convert_files(pt_files: List[Path], st_files: List[Path]):
+    assert len(pt_files) == len(st_files)
+
+    executor = ThreadPoolExecutor(max_workers=5)
+    futures = [
+        executor.submit(convert_file, pt_file=pt_file, st_file=st_file)
+        for pt_file, st_file in zip(pt_files, st_files)
+    ]
+
+    # We do this instead of using tqdm because we want to parse the logs with the launcher
+    start_time = time.time()
+    for i, future in enumerate(concurrent.futures.as_completed(futures)):
+        elapsed = timedelta(seconds=int(time.time() - start_time))
+        remaining = len(futures) - (i + 1)
+        eta = (elapsed / (i + 1)) * remaining if remaining > 0 else 0
+
+        logger.info(f"Convert: [{i + 1}/{len(futures)}] -- ETA: {eta}")
diff --git a/server/text_generation_server/utils/dist.py b/server/text_generation_server/utils/dist.py
new file mode 100644
index 0000000000000000000000000000000000000000..9785493eb8f9e44d24e4a89ca8e1dc55d68a1c6b
--- /dev/null
+++ b/server/text_generation_server/utils/dist.py
@@ -0,0 +1,35 @@
+import os
+import torch
+
+from datetime import timedelta
+
+
+def initialize_torch_distributed():
+    rank = int(os.getenv("RANK", "0"))
+    world_size = int(os.getenv("WORLD_SIZE", "1"))
+
+    if torch.cuda.is_available():
+        from torch.distributed import ProcessGroupNCCL
+
+        # Set the device id.
+        assert world_size <= torch.cuda.device_count(), "Each process is one gpu"
+        device = rank % torch.cuda.device_count()
+        torch.cuda.set_device(device)
+        backend = "nccl"
+        options = ProcessGroupNCCL.Options()
+        options.is_high_priority_stream = True
+        options._timeout = timedelta(seconds=60)
+    else:
+        backend = "gloo"
+        options = None
+
+    # Call the init process.
+    torch.distributed.init_process_group(
+        backend=backend,
+        world_size=world_size,
+        rank=rank,
+        timeout=timedelta(seconds=60),
+        pg_options=options,
+    )
+
+    return torch.distributed.group.WORLD, rank, world_size
diff --git a/server/text_generation_server/utils/hub.py b/server/text_generation_server/utils/hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..4feec8a1fc1ec3a63c8c85f0a57376e227549d67
--- /dev/null
+++ b/server/text_generation_server/utils/hub.py
@@ -0,0 +1,160 @@
+import time
+import os
+
+from datetime import timedelta
+from loguru import logger
+from pathlib import Path
+from typing import Optional, List
+
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
+from huggingface_hub.utils import (
+    LocalEntryNotFoundError,
+    EntryNotFoundError,
+    RevisionNotFoundError,  # Import here to ease try/except in other part of the lib
+)
+
+WEIGHTS_CACHE_OVERRIDE = os.getenv("WEIGHTS_CACHE_OVERRIDE", None)
+
+
+def weight_hub_files(
+    model_id: str, revision: Optional[str] = None, extension: str = ".safetensors"
+) -> List[str]:
+    """Get the weights filenames on the hub"""
+    api = HfApi()
+    info = api.model_info(model_id, revision=revision)
+    filenames = [s.rfilename for s in info.siblings if s.rfilename.endswith(extension)]
+
+    if not filenames:
+        raise EntryNotFoundError(
+            f"No {extension} weights found for model {model_id} and revision {revision}.",
+            None,
+        )
+
+    return filenames
+
+
+def try_to_load_from_cache(
+    model_id: str, revision: Optional[str], filename: str
+) -> Optional[Path]:
+    """Try to load a file from the Hugging Face cache"""
+    if revision is None:
+        revision = "main"
+
+    object_id = model_id.replace("/", "--")
+    repo_cache = Path(HUGGINGFACE_HUB_CACHE) / f"models--{object_id}"
+
+    if not repo_cache.is_dir():
+        # No cache for this model
+        return None
+
+    refs_dir = repo_cache / "refs"
+    snapshots_dir = repo_cache / "snapshots"
+
+    # Resolve refs (for instance to convert main to the associated commit sha)
+    if refs_dir.is_dir():
+        revision_file = refs_dir / revision
+        if revision_file.exists():
+            with revision_file.open() as f:
+                revision = f.read()
+
+    # Check if revision folder exists
+    if not snapshots_dir.exists():
+        return None
+    cached_shas = os.listdir(snapshots_dir)
+    if revision not in cached_shas:
+        # No cache for this revision and we won't try to return a random revision
+        return None
+
+    # Check if file exists in cache
+    cached_file = snapshots_dir / revision / filename
+    return cached_file if cached_file.is_file() else None
+
+
+def weight_files(
+    model_id: str, revision: Optional[str] = None, extension: str = ".safetensors"
+) -> List[Path]:
+    """Get the local files"""
+    # Local model
+    if Path(model_id).exists() and Path(model_id).is_dir():
+        return list(Path(model_id).glob(f"*{extension}"))
+
+    try:
+        filenames = weight_hub_files(model_id, revision, extension)
+    except EntryNotFoundError as e:
+        if extension != ".safetensors":
+            raise e
+        # Try to see if there are pytorch weights
+        pt_filenames = weight_hub_files(model_id, revision, extension=".bin")
+        # Change pytorch extension to safetensors extension
+        # It is possible that we have safetensors weights locally even though they are not on the
+        # hub if we converted weights locally without pushing them
+        filenames = [
+            f"{Path(f).stem.lstrip('pytorch_')}.safetensors" for f in pt_filenames
+        ]
+
+    if WEIGHTS_CACHE_OVERRIDE is not None:
+        files = []
+        for filename in filenames:
+            p = Path(WEIGHTS_CACHE_OVERRIDE) / filename
+            if not p.exists():
+                raise LocalEntryNotFoundError(
+                    f"File {p} not found in {WEIGHTS_CACHE_OVERRIDE}."
+                )
+            files.append(p)
+        return files
+
+    files = []
+    for filename in filenames:
+        cache_file = try_to_load_from_cache(
+            model_id, revision=revision, filename=filename
+        )
+        if cache_file is None:
+            raise LocalEntryNotFoundError(
+                f"File {filename} of model {model_id} not found in "
+                f"{os.getenv('HUGGINGFACE_HUB_CACHE', 'the local cache')}. "
+                f"Please run `text-generation-server download-weights {model_id}` first."
+            )
+        files.append(cache_file)
+
+    return files
+
+
+def download_weights(
+    filenames: List[str], model_id: str, revision: Optional[str] = None
+) -> List[Path]:
+    """Download the safetensors files from the hub"""
+
+    def download_file(filename):
+        local_file = try_to_load_from_cache(model_id, revision, filename)
+        if local_file is not None:
+            logger.info(f"File {filename} already present in cache.")
+            return Path(local_file)
+
+        logger.info(f"Download file: {filename}")
+        start_time = time.time()
+        local_file = hf_hub_download(
+            filename=filename,
+            repo_id=model_id,
+            revision=revision,
+            local_files_only=False,
+        )
+        logger.info(
+            f"Downloaded {local_file} in {timedelta(seconds=int(time.time() - start_time))}."
+        )
+        return Path(local_file)
+
+    # We do this instead of using tqdm because we want to parse the logs with the launcher
+    start_time = time.time()
+    files = []
+    for i, filename in enumerate(filenames):
+        file = download_file(filename)
+
+        elapsed = timedelta(seconds=int(time.time() - start_time))
+        remaining = len(filenames) - (i + 1)
+        eta = (elapsed / (i + 1)) * remaining if remaining > 0 else 0
+
+        logger.info(f"Download: [{i + 1}/{len(filenames)}] -- ETA: {eta}")
+        files.append(file)
+
+    return files
diff --git a/server/text_generation_server/utils/tokens.py b/server/text_generation_server/utils/tokens.py
new file mode 100644
index 0000000000000000000000000000000000000000..23f504c660422c856cbd2f4ee6b2617cb6c4e8bd
--- /dev/null
+++ b/server/text_generation_server/utils/tokens.py
@@ -0,0 +1,166 @@
+import re
+import torch
+
+from transformers import (
+    LogitsProcessorList,
+    TemperatureLogitsWarper,
+    TopKLogitsWarper,
+    TopPLogitsWarper,
+    TypicalLogitsWarper,
+    RepetitionPenaltyLogitsProcessor,
+    PreTrainedTokenizerBase,
+)
+from typing import List, Tuple, Optional
+
+from text_generation_server.pb import generate_pb2
+from text_generation_server.pb.generate_pb2 import FinishReason
+from text_generation_server.utils.watermark import WatermarkLogitsProcessor
+
+
+class Sampling:
+    def __init__(self, seed: int, device: str = "cpu"):
+        self.generator = torch.Generator(device)
+        self.generator.manual_seed(seed)
+        self.seed = seed
+
+    def __call__(self, logits):
+        probs = torch.nn.functional.softmax(logits, -1)
+        next_tokens = torch.multinomial(probs, num_samples=1, generator=self.generator)
+        return next_tokens
+
+
+class Greedy:
+    def __call__(self, logits):
+        return logits.argmax()
+
+
+class NextTokenChooser:
+    def __init__(
+        self,
+        watermark=False,
+        temperature=1.0,
+        repetition_penalty=1.0,
+        top_k=None,
+        top_p=None,
+        typical_p=None,
+        do_sample=False,
+        seed=0,
+        device="cpu",
+    ):
+        warpers = LogitsProcessorList()
+        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
+        # all samplers can be found in `generation_utils_samplers.py`
+        sampling = do_sample
+
+        if watermark:
+            warpers.append(WatermarkLogitsProcessor(device=device))
+        if repetition_penalty is not None and repetition_penalty != 1.0:
+            warpers.append(RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty))
+        if temperature is not None and temperature != 1.0:
+            temperature = float(temperature)
+            warpers.append(TemperatureLogitsWarper(temperature))
+            sampling = True
+        if top_k is not None and top_k != 0:
+            warpers.append(TopKLogitsWarper(top_k=top_k))
+            sampling = True
+        if top_p is not None and top_p < 1.0:
+            warpers.append(TopPLogitsWarper(top_p=top_p))
+            sampling = True
+        if typical_p is not None and typical_p < 1.0:
+            warpers.append(TypicalLogitsWarper(mass=typical_p))
+            sampling = True
+
+        self.warpers = warpers
+        self.choice = Sampling(seed, device) if sampling else Greedy()
+
+    def __call__(self, input_ids, scores):
+        # Warp logits
+        if scores.shape[0] > 1:
+            # only warp the last token logits
+            scores[-1:, :] = self.warpers(input_ids, scores[-1:, :])
+        else:
+            scores = self.warpers(input_ids, scores)
+
+        # Compute logprobs
+        logprobs = torch.log_softmax(scores, -1)
+
+        # Choose tokens
+        next_id = self.choice(scores[-1])
+
+        return next_id.view(1, 1), logprobs
+
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.NextTokenChooserParameters,
+        device: torch.device,
+    ) -> "NextTokenChooser":
+        return NextTokenChooser(
+            watermark=pb.watermark,
+            temperature=pb.temperature,
+            repetition_penalty=pb.repetition_penalty,
+            top_k=pb.top_k,
+            top_p=pb.top_p,
+            typical_p=pb.typical_p,
+            do_sample=pb.do_sample,
+            seed=pb.seed,
+            device=device,
+        )
+
+
+class StopSequenceCriteria:
+    def __init__(self, stop_sequence: str):
+        stop_sequence = re.escape(stop_sequence)
+        self.regex = re.compile(f".*{stop_sequence}$")
+
+    def __call__(self, output: str) -> bool:
+        if self.regex.findall(output):
+            return True
+        return False
+
+
+class StoppingCriteria:
+    def __init__(
+        self,
+        eos_token_id: int,
+        stop_sequence_criterias: List[StopSequenceCriteria],
+        max_new_tokens: int = 20,
+        ignore_eos_token: bool = False,
+    ):
+        self.eos_token_id = eos_token_id
+        self.stop_sequence_criterias = stop_sequence_criterias
+        self.max_new_tokens = max_new_tokens
+        self.current_tokens = 0
+        self.current_output = ""
+        self.ignore_eos_token = ignore_eos_token
+
+    def __call__(self, last_token: int, last_output: str) -> Tuple[bool, Optional[str]]:
+        self.current_tokens += 1
+        if self.current_tokens >= self.max_new_tokens:
+            return True, FinishReason.FINISH_REASON_LENGTH
+
+        if not self.ignore_eos_token and last_token == self.eos_token_id:
+            return True, FinishReason.FINISH_REASON_EOS_TOKEN
+
+        self.current_output += last_output
+        for stop_sequence_criteria in self.stop_sequence_criterias:
+            if stop_sequence_criteria(self.current_output):
+                return True, FinishReason.FINISH_REASON_STOP_SEQUENCE
+
+        return False, None
+
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.StoppingCriteriaParameters,
+        tokenizer: PreTrainedTokenizerBase,
+    ) -> "StoppingCriteria":
+        stop_sequence_criterias = [
+            StopSequenceCriteria(sequence) for sequence in pb.stop_sequences
+        ]
+        return StoppingCriteria(
+            tokenizer.eos_token_id,
+            stop_sequence_criterias,
+            pb.max_new_tokens,
+            pb.ignore_eos_token,
+        )
diff --git a/server/text_generation_server/utils/watermark.py b/server/text_generation_server/utils/watermark.py
new file mode 100644
index 0000000000000000000000000000000000000000..df7b90e399e9ca11691f8d578af59a95e58ded18
--- /dev/null
+++ b/server/text_generation_server/utils/watermark.py
@@ -0,0 +1,98 @@
+# coding=utf-8
+# Copyright 2023 Authors of "A Watermark for Large Language Models"
+# available at https://arxiv.org/abs/2301.10226
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import torch
+from transformers import LogitsProcessor
+from typing import List, Union
+
+GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
+DELTA = os.getenv("WATERMARK_DELTA", 2.0)
+
+
+class WatermarkLogitsProcessor(LogitsProcessor):
+    def __init__(
+        self,
+        gamma: float = GAMMA,
+        delta: float = DELTA,
+        hash_key: int = 15485863,  # just a large prime number to create a rng seed with sufficient bit width
+        device: str = "cpu",
+    ):
+        # watermarking parameters
+        self.gamma = gamma
+        self.delta = delta
+        self.rng = torch.Generator(device=device)
+        self.hash_key = hash_key
+
+    def _seed_rng(self, input_ids: Union[List[int], torch.LongTensor]):
+        if isinstance(input_ids, list):
+            assert (
+                len(input_ids) >= 1
+            ), "requires at least a 1 token prefix sequence to seed rng"
+            prev_token = input_ids[-1]
+        else:
+            assert len(input_ids) == 1
+            input_ids = input_ids[0]
+            assert (
+                input_ids.shape[-1] >= 1
+            ), "requires at least a 1 token prefix sequence to seed rng"
+            prev_token = input_ids[-1].item()
+        self.rng.manual_seed(self.hash_key * prev_token)
+
+    def _get_greenlist_ids(
+        self,
+        input_ids: Union[List[int], torch.LongTensor],
+        max_value: int,
+        device: torch.device,
+    ) -> List[int]:
+        # seed the rng using the previous tokens/prefix
+        self._seed_rng(input_ids)
+
+        greenlist_size = int(max_value * self.gamma)
+        vocab_permutation = torch.randperm(max_value, device=device, generator=self.rng)
+        greenlist_ids = vocab_permutation[:greenlist_size]
+        return greenlist_ids
+
+    @staticmethod
+    def _calc_greenlist_mask(
+        scores: torch.FloatTensor, greenlist_token_ids
+    ) -> torch.BoolTensor:
+        green_tokens_mask = torch.zeros_like(scores)
+        green_tokens_mask[-1, greenlist_token_ids] = 1
+        final_mask = green_tokens_mask.bool()
+        return final_mask
+
+    @staticmethod
+    def _bias_greenlist_logits(
+        scores: torch.Tensor, greenlist_mask: torch.Tensor, greenlist_bias: float
+    ) -> torch.Tensor:
+        scores[greenlist_mask] = scores[greenlist_mask] + greenlist_bias
+        return scores
+
+    def __call__(
+        self, input_ids: Union[List[int], torch.LongTensor], scores: torch.FloatTensor
+    ) -> torch.FloatTensor:
+        greenlist_ids = self._get_greenlist_ids(
+            input_ids, scores.shape[-1], scores.device
+        )
+        green_tokens_mask = self._calc_greenlist_mask(
+            scores=scores, greenlist_token_ids=greenlist_ids
+        )
+
+        scores = self._bias_greenlist_logits(
+            scores=scores, greenlist_mask=green_tokens_mask, greenlist_bias=self.delta
+        )
+        return scores
diff --git a/supported_models.json b/supported_models.json
new file mode 100644
index 0000000000000000000000000000000000000000..8e9dd9e03bdc410e66b637ac535de17b5ce8b776
--- /dev/null
+++ b/supported_models.json
@@ -0,0 +1,9 @@
+[
+  "bigcode/santacoder",
+  "bigscience/bloom",
+  "bigscience/bloomz",
+  "EleutherAI/gpt-neox-20b",
+  "google/flan-ul2",
+  "google/flan-t5-xxl",
+  "OpenAssistant/oasst-sft-1-pythia-12b"
+]