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Duplicate from matthoffner/wizardcoder-ggml
Browse filesCo-authored-by: Matt Hoffner <matthoffner@users.noreply.huggingface.co>
- .gitattributes +34 -0
- Dockerfile +23 -0
- README.md +19 -0
- main.py +56 -0
- requirements.txt +11 -0
.gitattributes
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Dockerfile
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FROM python:latest
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ENV PYTHONUNBUFFERED 1
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EXPOSE 8000
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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WORKDIR $HOME/app
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COPY requirements.txt ./
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RUN pip install --upgrade pip && \
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pip install -r requirements.txt
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COPY --chown=user . $HOME/app
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RUN ls -al
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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README.md
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---
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title: wizardcoder
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emoji: π§ββοΈπ»π«
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sdk: docker
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app_port: 8000
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duplicated_from: matthoffner/wizardcoder-ggml
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---
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# WizardCoder-ggml
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## <a href="https://github.com/ggerganov/ggml" target="_blank">ggml</a>
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## <a href="https://github.com/marella/ctransformers" target="_blank">ctransformers</a>
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## [FastAPI Docs](https://matthoffner-wizardcoder-ggml.hf.space/docs)
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### Updates
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* Added /v1/chat/completions
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* [Start using ctransformers](https://github.com/marella/ctransformers)
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* [Added starcoder example](https://github.com/ggerganov/ggml/tree/master/examples/starcoder)
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main.py
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import fastapi
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import json
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import markdown
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import uvicorn
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from fastapi.responses import HTMLResponse
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from fastapi.middleware.cors import CORSMiddleware
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from sse_starlette.sse import EventSourceResponse
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from ctransformers import AutoModelForCausalLM
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from pydantic import BaseModel
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llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardCoder-15B-1.0-GGML",
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model_file="WizardCoder-15B-1.0.ggmlv3.q4_0.bin",
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model_type="starcoder")
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app = fastapi.FastAPI(title="WizardCoder")
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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@app.get("/")
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async def index():
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html_content = """
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<html>
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<head>
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</head>
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<body>
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<h1><a href="https://matthoffner-wizardcoder-ggml.hf.space/docs">FastAPI Docs</a></h1>
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<iframe
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src="https://matthoffner-monacopilot.hf.space"
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frameborder="0"
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width="850"
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height="450"
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></iframe>
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</body>
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</html>
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"""
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return HTMLResponse(content=html_content, status_code=200)
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class ChatCompletionRequest(BaseModel):
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prompt: str
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@app.post("/v1/chat/completions")
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async def chat(request: ChatCompletionRequest, response_mode=None):
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tokens = llm.tokenize(request.prompt)
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async def server_sent_events(chat_chunks, llm):
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for chat_chunk in llm.generate(chat_chunks):
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yield dict(data=json.dumps(llm.detokenize(chat_chunk)))
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yield dict(data="[DONE]")
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return EventSourceResponse(server_sent_events(tokens, llm))
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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requirements.txt
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uvicorn
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markdown
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fastapi
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loguru
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torch
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numpy
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transformers
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ctransformers
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accelerate
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langchain
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sse_starlette
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