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Duplicate from limcheekin/orca_mini_v3_7B-GGUF
Browse filesCo-authored-by: Lim Chee Kin <limcheekin@users.noreply.huggingface.co>
- .gitattributes +35 -0
- Dockerfile +35 -0
- LICENSE +21 -0
- README.md +21 -0
- index.html +37 -0
- main.py +27 -0
- start_server.sh +6 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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Dockerfile
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# Grab a fresh copy of the Python image
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FROM python:3.10-slim
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# Install build and runtime dependencies
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RUN apt-get update && \
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apt-get install -y \
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libopenblas-dev \
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ninja-build \
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build-essential \
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pkg-config \
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curl
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RUN pip install -U pip setuptools wheel && \
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CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" FORCE_CMAKE=1 pip install --verbose llama-cpp-python[server]
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# Download model
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RUN mkdir model && \
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curl -L https://huggingface.co/TheBloke/orca_mini_v3_7B-GGUF/resolve/main/orca_mini_v3_7b.Q4_K_M.gguf -o model/gguf-model.bin
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COPY ./start_server.sh ./
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COPY ./main.py ./
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COPY ./index.html ./
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# Make the server start script executable
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RUN chmod +x ./start_server.sh
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# Set environment variable for the host
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ENV HOST=0.0.0.0
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ENV PORT=7860
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# Expose a port for the server
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EXPOSE ${PORT}
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# Run the server start script
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CMD ["/bin/sh", "./start_server.sh"]
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LICENSE
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MIT License
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Copyright (c) 2023 Lim Chee Kin
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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title: orca_mini_v3_7B-GGUF (Q4_K_M)
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colorFrom: purple
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colorTo: blue
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sdk: docker
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models:
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- psmathur/orca_mini_v3_7b
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- TheBloke/orca_mini_v3_7B-GGUF
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tags:
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- inference api
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- openai-api compatible
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- llama-cpp-python
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- orca_mini_v3_7B
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- gguf
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pinned: false
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duplicated_from: limcheekin/orca_mini_v3_7B-GGUF
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---
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# orca_mini_v3_7B-GGUF (Q4_K_M)
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Please refer to the [index.html](index.html) for more information.
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index.html
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<!DOCTYPE html>
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<html>
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<head>
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<title>orca_mini_v3_7B-GGUF (Q4_K_M)</title>
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</head>
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<body>
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<h1>orca_mini_v3_7B-GGUF (Q4_K_M)</h1>
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<p>
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With the utilization of the
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<a href="https://github.com/abetlen/llama-cpp-python">llama-cpp-python</a>
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package, we are excited to introduce the GGUF model hosted in the Hugging
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Face Docker Spaces, made accessible through an OpenAI-compatible API. This
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space includes comprehensive API documentation to facilitate seamless
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integration.
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</p>
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<ul>
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<li>
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The API endpoint:
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<a href="https://limcheekin-orca-mini-v3-7b-gguf.hf.space/v1"
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>https://limcheekin-orca-mini-v3-7b-gguf.hf.space/v1</a
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>
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</li>
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<li>
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The API doc:
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<a href="https://limcheekin-orca-mini-v3-7b-gguf.hf.space/docs"
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>https://limcheekin-orca-mini-v3-7b-gguf.hf.space/docs</a
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>
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</li>
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</ul>
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<p>
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If you find this resource valuable, your support in the form of starring
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the space would be greatly appreciated. Your engagement plays a vital role
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in furthering the application for a community GPU grant, ultimately
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enhancing the capabilities and accessibility of this space.
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</p>
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</body>
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</html>
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main.py
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from llama_cpp.server.app import create_app, Settings
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from fastapi.responses import HTMLResponse
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import os
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app = create_app(
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Settings(
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n_threads=2, # set to number of cpu cores
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model="model/gguf-model.bin",
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embedding=False
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)
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)
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# Read the content of index.html once and store it in memory
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with open("index.html", "r") as f:
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content = f.read()
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@app.get("/", response_class=HTMLResponse)
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async def read_items():
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return content
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app,
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host=os.environ["HOST"],
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port=int(os.environ["PORT"])
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)
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start_server.sh
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#!/bin/sh
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# For mlock support
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ulimit -l unlimited
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python3 -B main.py
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