macadeliccc
commited on
Commit
•
bd2ae0b
1
Parent(s):
3ac4316
init
Browse files- Dockerfile +24 -0
- app.py +52 -0
- requirements.txt +2 -0
Dockerfile
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FROM python:3.10 as base
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# Set model
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ENV MODEL=macadeliccc/piccolo-math-2x7b-GGUF
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ENV QUANT=Q4_K_M
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ENV CHAT_TEMPLATE=chatml
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# Set the working directory
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WORKDIR /app
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# Install Python requirements
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COPY ./requirements.txt /app/
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RUN pip install --no-cache-dir --upgrade -r requirements.txt
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# Download model
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RUN MODEL_NAME_FILE=$(echo ${MODEL#*/} | tr '[:upper:]' '[:lower:]' | sed 's/-gguf$//') && \
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wget https://huggingface.co/${MODEL}/resolve/main/${MODEL_NAME_FILE}.${QUANT}.gguf -O model.gguf
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# Copy the rest of your application
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COPY . .
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# Command to run the application
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CMD ["python", "app.py"]
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app.py
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import os
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import json
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import gradio as gr
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from llama_cpp import Llama
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# Get environment variables
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model_id = os.getenv('MODEL')
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quant = os.getenv('QUANT')
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chat_template = os.getenv('CHAT_TEMPLATE')
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# Interface variables
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model_name = model_id.split('/')[1].split('-GGUF')[0]
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title = f"🐶🧮 {model_name}"
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description = f"Chat with <a href=\"https://huggingface.co/{model_id}\">{model_name}</a> in GGUF format ({quant})!"
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# Initialize the LLM
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llm = Llama(model_path="model.gguf",
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n_ctx=4096,
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n_threads=2,
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chat_format=chat_template)
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# Function for streaming chat completions
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def chat_stream_completion(message, history, system_prompt):
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messages_prompts = [{"role": "system", "content": system_prompt}]
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for human, assistant in history:
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messages_prompts.append({"role": "user", "content": human})
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messages_prompts.append({"role": "assistant", "content": assistant})
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messages_prompts.append({"role": "user", "content": message})
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response = llm.create_chat_completion(
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messages=messages_prompts,
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stream=True
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)
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message_repl = ""
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for chunk in response:
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if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
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message_repl = message_repl + chunk['choices'][0]["delta"]["content"]
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yield message_repl
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# Gradio chat interface
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gr.ChatInterface(
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fn=chat_stream_completion,
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title=title,
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description=description,
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additional_inputs=[gr.Textbox("You are helpful assistant.")],
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additional_inputs_accordion="📝 System prompt",
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examples=[
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["How is Fermats last theorem relevant in number theory?"],
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["What's 9+2-1?"],
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["A store sells apples for $1.50 each and bananas for $0.75 each. If a customer buys 4 apples and 3 bananas, how much do they spend in total?"]
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]
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).queue().launch(server_name="0.0.0.0")
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requirements.txt
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llama-cpp-python
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gradio
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