Anticloud/anticloud-text-generation-inference

TEXT_GENERATION_INFERENCE

Governed Anticloud packaging of the upstream project TEXT_GENERATION_INFERENCE in category FRONTIER_HARNESSES. check results: see ISOLATED_LAB_RESULTS. Every number below traces to a named file + run stamp; nothing is borrowed from other projects.

Upstream: https://github.com/huggingface/text-generation-inference · Upstream pin: b4adbf2f6e2e721280bd0ea5f91d70f7d033f5ed · Category: FRONTIER_HARNESSES · Vendor: Anticloud FZ LLE · Licence: Apache-2.0

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What This Project Does

text-generation-inference is now in maintenance mode. Going forward, we will accept pull requests for minor bug fixes, documentation improvements and lightweight maintenance tasks.

TGI has initiated the movement for optimized inference engines to rely on a transformers model architectures. This approach is now adopted by downstream inference engines, which we contribute to and recommend using going forward: vllm, SGLang, as well as local engines with inter-compatibility such as llama.cpp or MLX.

Text Generation Inference

A Rust, Python and gRPC server for text generation inference. Used in production at Hugging Face to power Hugging Chat, the Inference API and Inference Endpoints.

Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more. TGI implements many features, such as:

• Simple launcher to serve most popular LLMs
• Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
• Tensor Parallelism for faster inference on multiple GPUs
• Token streaming using Server-Sent Events (SSE)
• Continuous batching of incoming requests for increased total throughput
• Messages API compatible with Open AI Chat Completion API
• Optimized transformers code for inference using Flash Attention and Paged Attention on the most popular architectures
• Quantization with :
  • bitsandbytes
  • GPT-Q
  • EETQ
  • AWQ
  • Marlin
  • fp8
• Safetensors weight loading
• Watermarking with A Watermark for Large Language Models
• Logits warper (temperature scaling, top-p, top-k, repetition penalty, more details see transformers.LogitsProcessor)
• Stop sequences
• Log probabilities
• Speculation ~2x latency
• Guidance/JSON. Specify output format to speed up inference and make sure the output is valid according to some specs..
• Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output
• Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance

Hardware support

• Nvidia
• AMD (-rocm)
• Inferentia
• Intel GPU
• Gaudi
• Google TPU

Get Started

Docker

For a detailed starting guide, please see the Quick Tour. The easiest way of getting started is using the official Docker container:

model=HuggingFaceH4/zephyr-7b-beta
# share a volume with the Docker container to avoid downloading weights every run
volume=$PWD/data

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
    ghcr.io/huggingface/text-generation-inference:3.3.5 --model-id $model

And then you can make requests like

curl 127.0.0.1:8080/generate_stream \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
    -H 'Content-Type: application/json'

You can also use TGI's Messages API to obtain Open AI Chat Completion API compatible responses.

curl localhost:8080/v1/chat/completions \
    -X POST \
    -d '{
  "model": "tgi",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "What is deep learning?"
    }
  ],
  "stream": true,
  "max_tokens": 20
}' \
    -H 'Content-Type: application/json'

Note: To use NVIDIA GPUs, you need to install the NVIDIA Container Toolkit. We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the --gpus all flag and add --disable-custom-kernels, please note CPU is not the intended platform for this project, so performance might be subpar.

Note: TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the Supported Hardware documentation. To use AMD GPUs, please use docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.3.5-rocm --model-id $model instead of the command above.

To see all options to serve your models (in the code or in the cli):

text-generation-launcher --help

API documentation

You can consult the OpenAPI documentation of the text-generation-inference REST API using the /docs route. The Swagger UI is also available at: https://huggingface.github.io/text-generation-inference.

Using a private or gated model

You have the option to utilize the HF_TOKEN environment variable for configuring the token employed by text-generation-inference. This allows you to gain access to protected resources.

For example, if you want to serve the gated Llama V2 model variants:

1. Go to https://huggingface.co/settings/tokens
2. Copy your CLI READ token
3. Export HF_TOKEN=<your CLI READ token>

or with Docker:

model=meta-llama/Meta-Llama-3.1-8B-Instruct
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>

docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data \
    ghcr.io/huggingface/text-generation-inference:3.3.5 --model-id $model

A note on Shared Memory (shm)

NCCL is a communication framework used by PyTorch to do distributed training/inference. text-generation-inference makes use of NCCL to enable Tensor Parallelism to dramatically speed up inference for large language models.

In order to share data between the different devices of a NCCL group, NCCL might fall back to using the host memory if peer-to-peer using NVLink or PCI is not possible.

To allow the container to use 1G of Shared Memory and support SHM sharing, we add --shm-size 1g on the above command.

If you are running text-generation-inference inside Kubernetes. You can also add Shared Memory to the container by creating a volume with:

- name: shm
  emptyDir:
   medium: Memory
   sizeLimit: 1Gi

and mounting it to /dev/shm.

Finally, you can also disable SHM sharing by using the NCCL_SHM_DISABLE=1 environment variable. However, note that this will impact performance.

Distributed Tracing

text-generation-inference is instrumented with distributed tracing using OpenTelemetry. You can use this feature by setting the address to an OTLP collector with the --otlp-endpoint argument. The default service name can be overridden with the --otlp-service-name argument

Architecture

Detailed blogpost by Adyen on TGI inner workings: LLM inference at scale with TGI (Martin Iglesias Goyanes - Adyen, 2024)

Local install

You can also opt to install text-generation-inference locally.

First source the project and change directory into it:

git source https://github.com/huggingface/text-generation-inference
cd text-generation-inference

Then install Rust and create a Python virtual environment with at least Python 3.9, e.g. using conda or python venv:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

#using conda
conda create -n text-generation-inference python=3.11
conda activate text-generation-inference

#using python venv
python3 -m venv .venv
source .venv/bin/activate

You may also need to install Protoc.

On Linux:

PROTOC_ZIP=protoc-21.12-linux-x86_64.zip
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
rm -f $PROTOC_ZIP

On MacOS, using Homebrew:

brew install protobuf

Then run:

BUILD_EXTENSIONS=True make install # Install project and HF/transformer fork with CUDA kernels
text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2

Note: on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:

sudo apt-get install libssl-dev gcc -y

Local install (Nix)

Another option is to install text-generation-inference locally using Nix. Currently, we only support Nix on x86_64 Linux with CUDA GPUs. When using Nix, all dependencies can be pulled from a binary cache, removing the need to build them locally.

First follow the instructions to install Cachix and enable the Hugging Face cache. Setting up the cache is important, otherwise Nix will build many of the dependencies locally, which can take hours.

After that you can run TGI with nix run:

cd text-generation-inference
nix run --extra-experimental-features nix-command --extra-experimental-features flakes . -- --model-id meta-llama/Llama-3.1-8B-Instruct

Note: when you are using Nix on a non-NixOS system, you have to make some symlinks to make the CUDA driver libraries visible to Nix packages.

For TGI development, you can use the impure dev shell:

nix develop .#impure

# Only needed the first time the devshell is started or after updating the protobuf.
(
cd server
mkdir text_generation_server/pb || true
python -m grpc_tools.protoc -I../proto/v3 --python_out=text_generation_server/pb \
       --grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/v3/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
)

All development dependencies (cargo, Python, Torch), etc. are available in this dev shell.

Optimized architectures

TGI works out of the box to serve optimized models for all modern models. They can be found in this list.

Other architectures are supported on a best-effort basis using:

AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")

or

AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")

Run locally

Run

text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2

Quantization

You can also run pre-quantized weights (AWQ, GPTQ, Marlin) or on-the-fly quantize weights with bitsandbytes, EETQ, fp8, to reduce the VRAM requirement:

text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2 --quantize

4bit quantization is available using the NF4 and FP4 data types from bitsandbytes. It can be enabled by providing --quantize bitsandbytes-nf4 or --quantize bitsandbytes-fp4 as a command line argument to text-generation-launcher.

Read more about quantization in the Quantization documentation.

Develop

make server-dev
make router-dev

Testing

# python
make python-server-tests
make python-client-tests
# or both server and client tests
make python-tests
# rust cargo tests
make rust-tests
# integration tests
make integration-tests

---

Installation

• Local install (Nix)
  • Optimized architectures
  • Run locally
    • Run
    • Quantization
  • Develop
  • Testing

Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more. TGI implements many features, such as:

• Simple launcher to serve most popular LLMs
• Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
• Tensor Parallelism for faster inference on multiple GPUs
• Token streaming using Server-Sent Events (SSE)
• Continuous batching of incoming requests for increased total throughput
• Messages API compatible with Open AI Chat Completion API
• Optimized transformers code for inference using Flash Attention and Paged Attention on the most popular architectures
• Quantization with :
  • bitsandbytes
  • GPT-Q
  • EETQ
  • AWQ
  • Marlin
  • fp8
• Safetensors weight loading
• Watermarking with A Watermark for Large Language Models
• Logits warper (temperature scaling, top-p, top-k, repetition penalty, more details see transformers.LogitsProcessor)
• Stop sequences
• Log probabilities
• Speculation ~2x latency
• Guidance/JSON. Specify output format to speed up inference and make sure the output is valid according to some specs..
• Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output
• Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance

Usage

to power Hugging Chat, the Inference API and Inference Endpoints.

API

TGI has initiated the movement for optimized inference engines to rely on a transformers model architectures. This approach is now adopted by downstream inference engines, which we contribute to and recommend using going forward: vllm, SGLang, as well as local engines with inter-compatibility such as llama.cpp or MLX.

Dependencies

Metric	Value
Files	unknown
Lines of Code	unknown
Dependencies	unknown
Upstream license (harvested)	Apache-2.0
Overlay license	Anticommons 0.1.0

Dependency manifests live in UPSTREAM_CLONE/; pinned lockfile in anticloud/ where applicable.

Configuration

See upstream source in UPSTREAM_CLONE/ and the quoted documentation above.

Contributing

Contribute to text-generation-inference

Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable.

It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the project to say thank you.

However you choose to contribute, please be mindful and respect our code of conduct.

This guide was heavily inspired by the awesome scikit-learn guide to contributing.

Ways to contribute

There are several ways you can contribute to text-generation-inference.

• Fix outstanding issues with the existing code.
• Submit issues related to bugs or desired new features.
• Contribute to the examples or to the documentation.

All contributions are equally valuable to the community. 🥰

Fixing outstanding issues

If you notice an issue with the existing code and have a fix in mind, feel free to start contributing and open a Pull Request!

Submitting a bug-related issue or feature request

Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.

Did you find a bug?

The text-generation-inference library is robust and reliable thanks to users who report the problems they encounter.

Before you report an issue, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code.

Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:

• Your OS type and version, as well as your environment versions (versions of rust, python, and dependencies).
• A short, self-contained, code snippet that allows us

License

Upstream © its respective contributors under Apache-2.0 (harvested MIT/Apache-2.0/BSD source; see UPSTREAM_CLONE/LICENSE). This packaging overlay is licensed under Anticommons 0.1.0.

Upstream

• project: https://github.com/huggingface/text-generation-inference
• Pinned SHA: b4adbf2f6e2e721280bd0ea5f91d70f7d033f5ed
• source: UPSTREAM_CLONE/ (pinned at the SHA above)
• Upstream README source: UPSTREAM_CLONE/README.md

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 0be985ffc0046226601eb68848530a4a0d71bdef5a362e51b6fdd6c0ae1c7d24.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.