Update Dockerfile

Dockerfile

@@ -1,106 +1,24 @@
-# Dockerfile for Qwen2.5-Coder-7B-Instruct-GGUF on Hugging Face Spaces (Free Tier)
-# Optimized for: 2 vCPU, 16GB RAM, CPU-only
-# Uses llama-cpp-python for OpenAI-compatible API
+FROM ghcr.io/ggml-org/llama.cpp:full
 
-FROM python:3.11-slim-bookworm
-
-# Install system dependencies
+# Install wget and other dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    cmake \
-    curl \
-    libopenblas-dev \
+    wget \
     && rm -rf /var/lib/apt/lists/*
 
-# Set environment variables for build optimization
-ENV CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS"
-ENV FORCE_CMAKE=1
-
-# Install llama-cpp-python with OpenBLAS acceleration (CPU-optimized)
-RUN pip install --no-cache-dir llama-cpp-python==0.3.2 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
-
-# Install huggingface-hub for model download
-RUN pip install --no-cache-dir huggingface-hub gradio
-
-# Set working directory
-WORKDIR /app
-
-# Download Q4_K_M quantized model (smallest viable for coding tasks)
-# Q4_K_M: ~4.7GB, good balance for 16GB RAM limit
-RUN python3 -c "from huggingface_hub import hf_hub_download; \
-    hf_hub_download( \
-        repo_id='Qwen/Qwen2.5-Coder-7B-Instruct-GGUF', \
-        filename='qwen2.5-coder-7b-instruct-q4_k_m.gguf', \
-        local_dir='/app/models', \
-        local_dir_use_symlinks=False \
-    )"
-
-# Create Gradio app.py for Hugging Face Spaces interface
-RUN cat > /app/app.py << 'EOF'
-import gradio as gr
-from llama_cpp import Llama
-import os
+# Download the specific GGUF file (not the HTML page)
+# Using Q4_K_M for optimal balance on free tier (2 vCPU, 16GB RAM)
+RUN wget --no-check-certificate "https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct-GGUF/resolve/main/qwen2.5-coder-7b-instruct-q4_k_m.gguf" \
+    -O /model.gguf
 
-# Initialize model with aggressive CPU optimization
-llm = Llama(
-    model_path="/app/models/qwen2.5-coder-7b-instruct-q4_k_m.gguf",
-    n_ctx=4096,           # Reduced context for memory efficiency
-    n_batch=512,          # Smaller batch size for 2 vCPU
-    n_threads=2,          # Match Hugging Face free tier vCPUs
-    n_threads_batch=2,
-    verbose=False,
-    use_mmap=True,        # Memory mapping for efficient loading
-    use_mlock=False       # Disable memory locking for container compatibility
-)
-
-SYSTEM_PROMPT = "You are Qwen2.5-Coder, a helpful AI coding assistant. Provide concise, accurate code solutions."
-
-def generate_code(message, history):
-    # Format with ChatML template
-    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
-    
-    # Add history
-    for human, assistant in history:
-        messages.append({"role": "user", "content": human})
-        messages.append({"role": "assistant", "content": assistant})
-    
-    messages.append({"role": "user", "content": message})
-    
-    # Generate with conservative parameters for free tier
-    output = llm.create_chat_completion(
-        messages=messages,
-        max_tokens=1024,      # Limit response length
-        temperature=0.7,
-        top_p=0.9,
-        repeat_penalty=1.1,
-        stop=["<|im_end|>", "<|im_start|>"]
-    )
-    
-    return output["choices"][0]["message"]["content"]
-
-# Create Gradio interface
-demo = gr.ChatInterface(
-    fn=generate_code,
-    title="Qwen2.5-Coder-7B",
-    description="Optimized for Hugging Face Spaces Free Tier (2 vCPU, 16GB RAM)",
-    examples=[
-        "Write a Python function to reverse a string",
-        "Explain how quicksort works with code",
-        "Create a simple REST API with Flask"
-    ],
-    cache_examples=False  # Disable to save memory
-)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-EOF
-
-# Expose Hugging Face Spaces port
+# Expose Hugging Face Spaces default port
 EXPOSE 7860
 
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
-    CMD curl -f http://localhost:7860/ || exit 1
-
-# Run the Gradio app
-CMD ["python3", "/app/app.py"]
+# Run llama.cpp server with optimized settings for free tier
+ENTRYPOINT ["/llama-server"]
+CMD ["-m", "/model.gguf", \
+     "--port", "7860", \
+     "--host", "0.0.0.0", \
+     "-c", "4096", \
+     "-n", "512", \
+     "--threads", "2", \
+     "--threads-batch", "2"]