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Duplicate from limcheekin/WizardCoder-Python-13B-V1.0-GGUF
Browse files- .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/WizardCoder-Python-13B-V1.0-GGUF/resolve/main/wizardcoder-python-13b-v1.0.Q5_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: WizardCoder-Python-13B-V1.0-GGUF (Q5_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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            - WizardLM/WizardCoder-Python-13B-V1.0
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            - TheBloke/WizardCoder-Python-13B-V1.0-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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            - WizardCoder-Python-13B-V1.0
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            - gguf
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            pinned: false
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            duplicated_from: limcheekin/WizardCoder-Python-13B-V1.0-GGUF
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            ---
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            # orca_mini_v3_7B-GGUF (Q5_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>WizardCoder-Python-13B-V1.0-GGUF (Q5_K_M)</title>
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              </head>
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              <body>
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                <h1>WizardCoder-Python-13B-V1.0-GGUF (Q5_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-wizardcoder-python-13b-v1-0-gguf.hf.space/v1"
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                      >https://limcheekin-wizardcoder-python-13b-v1-0-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-wizardcoder-python-13b-v1-0-gguf.hf.space/docs"
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                      >https://limcheekin-wizardcoder-python-13b-v1-0-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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