I'll help you create Docker files and set up hosting for the DeepCoder model. Let me create the necessary files for both Docker setup and potential Hugging Face deployment.

# Dockerfile for DeepCoder AI Model
FROM python:3.11-slim

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    curl \
    wget \
    git \
    && rm -rf /var/lib/apt/lists/*

# Install Docker Model Runner (assuming it's a Python package or CLI tool)
RUN pip install --no-cache-dir \
    torch \
    transformers \
    accelerate \
    bitsandbytes \
    huggingface_hub

# Create directories for model and cache
RUN mkdir -p /app/models /app/cache

# Set environment variables
ENV MODEL_NAME="ai/deepcoder-preview"
ENV MODEL_VARIANT="14B-Q4_K_M"
ENV HUGGINGFACE_HUB_CACHE="/app/cache"
ENV TRANSFORMERS_CACHE="/app/cache"

# Copy application files
COPY requirements.txt .
COPY app.py .
COPY download_model.py .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Download model during build (optional - can be done at runtime)
# RUN python download_model.py

# Expose port for API
EXPOSE 8000

# Health check
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:8000/health || exit 1

# Run the application
CMD ["python", "app.py"]

version: '3.8'

services:
  deepcoder-api:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: deepcoder-model
    ports:
      - "8000:8000"
    environment:
      - MODEL_NAME=ai/deepcoder-preview
      - MODEL_VARIANT=14B-Q4_K_M
      - HUGGINGFACE_HUB_CACHE=/app/cache
      - CUDA_VISIBLE_DEVICES=0
    volumes:
      - ./models:/app/models
      - ./cache:/app/cache
      - ./logs:/app/logs
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

  nginx:
    image: nginx:alpine
    container_name: deepcoder-nginx
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf
      - ./ssl:/etc/nginx/ssl
    depends_on:
      - deepcoder-api
    restart: unless-stopped

volumes:
  models:
  cache:
  logs:

#!/usr/bin/env python3
"""
DeepCoder Model API Server
Serves the DeepCoder-14B model via FastAPI
"""

import os
import asyncio
import logging
from typing import Optional, Dict, Any
import uvicorn
from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import hf_hub_download
import json

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration
MODEL_NAME = os.getenv("MODEL_NAME", "ai/deepcoder-preview")
MODEL_VARIANT = os.getenv("MODEL_VARIANT", "14B-Q4_K_M")
CACHE_DIR = os.getenv("HUGGINGFACE_HUB_CACHE", "/app/cache")
MAX_TOKENS = 131072  # 131K context length
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

app = FastAPI(
    title="DeepCoder API",
    description="AI Code Generation Model API",
    version="1.0.0"
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global model variables
tokenizer = None
model = None
model_loaded = False

class CodeRequest(BaseModel):
    prompt: str = Field(..., description="Code generation prompt")
    temperature: float = Field(0.6, ge=0.0, le=2.0, description="Sampling temperature")
    top_p: float = Field(0.95, ge=0.0, le=1.0, description="Top-p sampling")
    max_tokens: int = Field(2048, ge=1, le=8192, description="Maximum tokens to generate")
    stop_sequences: Optional[list] = Field(None, description="Stop sequences")

class CodeResponse(BaseModel):
    generated_code: str
    model_info: Dict[str, Any]
    generation_params: Dict[str, Any]

async def load_model():
    """Load the DeepCoder model and tokenizer"""
    global tokenizer, model, model_loaded
    
    if model_loaded:
        return
    
    try:
        logger.info(f"Loading model: {MODEL_NAME}")
        
        # Load tokenizer
        tokenizer = AutoTokenizer.from_pretrained(
            MODEL_NAME,
            cache_dir=CACHE_DIR,
            trust_remote_code=True
        )
        
        # Load model with appropriate settings for the quantized version
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            cache_dir=CACHE_DIR,
            trust_remote_code=True,
            torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
            device_map="auto" if DEVICE == "cuda" else None,
            load_in_4bit=True if "Q4" in MODEL_VARIANT else False,
        )
        
        if DEVICE == "cpu" and hasattr(model, 'to'):
            model = model.to(DEVICE)
            
        model_loaded = True
        logger.info(f"Model loaded successfully on {DEVICE}")
        
    except Exception as e:
        logger.error(f"Error loading model: {str(e)}")
        raise

@app.on_event("startup")
async def startup_event():
    """Load model on startup"""
    await load_model()

@app.get("/")
async def root():
    return {
        "message": "DeepCoder API",
        "model": MODEL_NAME,
        "variant": MODEL_VARIANT,
        "status": "ready" if model_loaded else "loading"
    }

@app.get("/health")
async def health_check():
    return {
        "status": "healthy" if model_loaded else "loading",
        "model_loaded": model_loaded,
        "device": DEVICE,
        "gpu_available": torch.cuda.is_available()
    }

@app.get("/model/info")
async def model_info():
    """Get model information"""
    if not model_loaded:
        raise HTTPException(status_code=503, detail="Model not loaded yet")
    
    return {
        "model_name": MODEL_NAME,
        "variant": MODEL_VARIANT,
        "max_context_length": MAX_TOKENS,
        "device": DEVICE,
        "model_size": "14B parameters",
        "quantization": "Q4_K_M" if "Q4" in MODEL_VARIANT else "None",
        "benchmarks": {
            "LiveCodeBench_v5_Pass@1": "60.6%",
            "Codeforces_Elo": 1936,
            "Codeforces_Percentile": "95.3",
            "HumanEval+_Accuracy": "92.6%"
        }
    }

@app.post("/generate", response_model=CodeResponse)
async def generate_code(request: CodeRequest):
    """Generate code using the DeepCoder model"""
    if not model_loaded:
        raise HTTPException(status_code=503, detail="Model not loaded yet")
    
    try:
        # Tokenize input
        inputs = tokenizer(
            request.prompt,
            return_tensors="pt",
            truncation=True,
            max_length=MAX_TOKENS - request.max_tokens
        )
        
        if DEVICE == "cuda":
            inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
        
        # Generation parameters
        generation_kwargs = {
            "max_new_tokens": request.max_tokens,
            "temperature": request.temperature,
            "top_p": request.top_p,
            "do_sample": True,
            "pad_token_id": tokenizer.eos_token_id,
        }
        
        if request.stop_sequences:
            generation_kwargs["stop_sequences"] = request.stop_sequences
        
        # Generate
        with torch.no_grad():
            outputs = model.generate(**inputs, **generation_kwargs)
        
        # Decode output
        generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]
        generated_code = tokenizer.decode(generated_tokens, skip_special_tokens=True)
        
        return CodeResponse(
            generated_code=generated_code,
            model_info={
                "model_name": MODEL_NAME,
                "variant": MODEL_VARIANT,
                "device": DEVICE
            },
            generation_params={
                "temperature": request.temperature,
                "top_p": request.top_p,
                "max_tokens": request.max_tokens
            }
        )
        
    except Exception as e:
        logger.error(f"Generation error: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")

@app.post("/chat")
async def chat_completion(request: CodeRequest):
    """Chat-style completion for code assistance"""
    # Add system context for better code generation
    system_prompt = """You are DeepCoder, an expert AI programming assistant. Generate high-quality, well-commented code that follows best practices."""
    
    full_prompt = f"{system_prompt}\n\nUser: {request.prompt}\n\nAssistant:"
    
    # Create modified request with system prompt
    modified_request = CodeRequest(
        prompt=full_prompt,
        temperature=request.temperature,
        top_p=request.top_p,
        max_tokens=request.max_tokens,
        stop_sequences=request.stop_sequences
    )
    
    return await generate_code(modified_request)

if __name__ == "__main__":
    uvicorn.run(
        "app:app",
        host="0.0.0.0",
        port=8000,
        reload=False,
        log_level="info"
    )

fastapi==0.104.1
uvicorn[standard]==0.24.0
torch>=2.0.0
transformers>=4.35.0
accelerate>=0.24.0
bitsandbytes>=0.41.0
huggingface_hub>=0.19.0
pydantic>=2.5.0
python-multipart==0.0.6
jinja2>=3.1.0
aiofiles>=23.0.0
nvidia-ml-py3>=7.352.0
psutil>=5.9.0
requests>=2.31.0

#!/usr/bin/env python3
"""
Download script for DeepCoder model
Downloads and caches the model for faster container startup
"""

import os
import logging
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import snapshot_download

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

MODEL_NAME = os.getenv("MODEL_NAME", "ai/deepcoder-preview")
CACHE_DIR = os.getenv("HUGGINGFACE_HUB_CACHE", "/app/cache")

def download_model():
    """Download the model and tokenizer"""
    try:
        logger.info(f"Downloading model: {MODEL_NAME}")
        
        # Download model files
        snapshot_download(
            repo_id=MODEL_NAME,
            cache_dir=CACHE_DIR,
            resume_download=True
        )
        
        # Verify by loading tokenizer
        tokenizer = AutoTokenizer.from_pretrained(
            MODEL_NAME,
            cache_dir=CACHE_DIR,
            trust_remote_code=True
        )
        
        logger.info("Model downloaded successfully")
        logger.info(f"Vocab size: {tokenizer.vocab_size}")
        logger.info(f"Cache directory: {CACHE_DIR}")
        
        return True
        
    except Exception as e:
        logger.error(f"Error downloading model: {str(e)}")
        return False

if __name__ == "__main__":
    success = download_model()
    if not success:
        exit(1)
    logger.info("Download complete!")

events {
    worker_connections 1024;
}

http {
    upstream deepcoder_backend {
        server deepcoder-api:8000;
    }
    
    # Rate limiting
    limit_req_zone $binary_remote_addr zone=api:10m rate=10r/m;
    
    server {
        listen 80;
        server_name localhost;
        
        # Security headers
        add_header X-Frame-Options DENY;
        add_header X-Content-Type-Options nosniff;
        add_header X-XSS-Protection "1; mode=block";
        
        # Increase client max body size for large code submissions
        client_max_body_size 10M;
        
        # Timeouts for long-running generation requests
        proxy_connect_timeout 60s;
        proxy_send_timeout 300s;
        proxy_read_timeout 300s;
        
        location / {
            proxy_pass http://deepcoder_backend;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Forwarded-Proto $scheme;
        }
        
        location /generate {
            limit_req zone=api burst=5 nodelay;
            proxy_pass http://deepcoder_backend;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Forwarded-Proto $scheme;
        }
        
        location /health {
            proxy_pass http://deepcoder_backend;
            access_log off;
        }
    }
}

#!/bin/bash
# setup.sh - Setup script for DeepCoder deployment

set -e

echo "🚀 DeepCoder Model Setup"
echo "========================"

# Create necessary directories
echo "📁 Creating directories..."
mkdir -p models cache logs ssl

# Set permissions
chmod 755 models cache logs
chmod 700 ssl

# Pull the DeepCoder model using Docker Model Runner
echo "📦 Pulling DeepCoder model..."
if command -v docker &> /dev/null; then
    # Assuming docker model runner is available
    docker model pull ai/deepcoder-preview
else
    echo "⚠️  Docker not found. Please install Docker first."
    exit 1
fi

# Check for GPU support
echo "🔍 Checking GPU support..."
if command -v nvidia-smi &> /dev/null; then
    echo "✅ NVIDIA GPU detected:"
    nvidia-smi --query-gpu=gpu_name,memory.total --format=csv,noheader
    
    # Check for Docker GPU support
    if docker run --rm --gpus all nvidia/cuda:11.8-base nvidia-smi &> /dev/null; then
        echo "✅ Docker GPU support verified"
        export GPU_SUPPORT=true
    else
        echo "⚠️  Docker GPU support not available"
        export GPU_SUPPORT=false
    fi
else
    echo "⚠️  No GPU detected. Running on CPU."
    export GPU_SUPPORT=false
fi

# Build and start containers
echo "🏗️  Building Docker containers..."
docker-compose build

echo "🚀 Starting services..."
if [ "$GPU_SUPPORT" = true ]; then
    docker-compose up -d
else
    # Remove GPU requirements for CPU-only deployment
    sed 's/devices:/# devices:/g' docker-compose.yml | \
    sed 's/- driver: nvidia/# - driver: nvidia/g' | \
    sed 's/count: 1/# count: 1/g' | \
    sed 's/capabilities: \[gpu\]/# capabilities: [gpu]/g' > docker-compose-cpu.yml
    docker-compose -f docker-compose-cpu.yml up -d
fi

# Wait for services to be ready
echo "⏳ Waiting for services to start..."
sleep 30

# Health check
echo "🏥 Performing health check..."
for i in {1..10}; do
    if curl -f http://localhost:8000/health > /dev/null 2>&1; then
        echo "✅ DeepCoder API is healthy!"
        break
    else
        echo "⏳ Waiting for API to be ready... (attempt $i/10)"
        sleep 10
    fi
done

# Show status
echo "📊 Service Status:"
docker-compose ps

echo ""
echo "🎉 DeepCoder setup complete!"
echo "API endpoint: http://localhost:8000"
echo "Health check: http://localhost:8000/health"
echo "Model info: http://localhost:8000/model/info"
echo ""
echo "To test the API:"
echo "curl -X POST http://localhost:8000/generate \\"
echo "  -H 'Content-Type: application/json' \\"
echo "  -d '{\"prompt\": \"def fibonacci(n):\", \"max_tokens\": 200}'"

###########################################
# deploy-hf.sh - Hugging Face Spaces deployment
###########################################

cat > deploy-hf.sh << 'EOL'
#!/bin/bash
# Deploy to Hugging Face Spaces

set -e

echo "🤗 Deploying to Hugging Face Spaces"
echo "===================================="

# Check if git is configured
if ! git config user.email > /dev/null; then
    echo "⚠️  Please configure git:"
    echo "git config --global user.email 'your-email@example.com'"
    echo "git config --global user.name 'Your Name'"
    exit 1
fi

# Check if HF_TOKEN is set
if [ -z "$HF_TOKEN" ]; then
    echo "⚠️  Please set your Hugging Face token:"
    echo "export HF_TOKEN=your_hf_token_here"
    exit 1
fi

SPACE_NAME=${1:-"deepcoder-api"}
HF_USERNAME=${2:-$(whoami)}

echo "Creating Space: $HF_USERNAME/$SPACE_NAME"

# Create Hugging Face Space files
cat > README.md << EOF
---
title: DeepCoder API
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: mit
---

# DeepCoder API

High-performance code generation API powered by DeepCoder-14B model.

## Features
- 🎯 60.6% pass rate on LiveCodeBench v5
- 🏆 1936 Elo rating on Codeforces (95.3 percentile)  
- 📝 92.6% accuracy on HumanEval+
- ⚡ 131K token context length
- 🔧 Optimized Q4_K_M quantization

## API Endpoints
- \`POST /generate\` - Generate code from prompts
- \`POST /chat\` - Chat-style code assistance  
- \`GET /model/info\` - Model information
- \`GET /health\` - Health check

## Usage
\`\`\`bash
curl -X POST /generate \\
  -H 'Content-Type: application/json' \\
  -d '{"prompt": "def fibonacci(n):", "max_tokens": 200}'
\`\`\`
EOF

# Create Dockerfile for HF Spaces
cat > Dockerfile.hf << EOF
FROM python:3.11-slim

WORKDIR /app

RUN apt-get update && apt-get install -y curl git && rm -rf /var/lib/apt/lists/*

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 7860

CMD ["python", "-m", "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
EOF

# Update app.py for HF Spaces (port 7860)
sed 's/port=8000/port=7860/g' app.py > app_hf.py
mv app_hf.py app.py

# Initialize git repo if not exists
if [ ! -d .git ]; then
    git init
    git lfs install
fi

# Track large model files with git LFS
echo "*.bin filter=lfs diff=lfs merge=lfs -text" >> .gitattributes
echo "*.safetensors filter=lfs diff=lfs merge=lfs -text" >> .gitattributes

# Add remote if not exists
if ! git remote get-url origin > /dev/null 2>&1; then
    git remote add origin https://huggingface.co/spaces/$HF_USERNAME/$SPACE_NAME
fi

# Commit and push
git add .
git commit -m "Initial DeepCoder API deployment" || true
git push -u origin main

echo "✅ Deployed to: https://huggingface.co/spaces/$HF_USERNAME/$SPACE_NAME"
EOL

chmod +x deploy-hf.sh

echo "📝 Additional deployment script created: deploy-hf.sh"