# Replicate Setup Instructions

## Prerequisites
1. Install Cog: https://github.com/replicate/cog
   ```bash
   sudo curl -o /usr/local/bin/cog -L https://github.com/replicate/cog/releases/latest/download/cog_`uname -s`_`uname -m`
   sudo chmod +x /usr/local/bin/cog
   ```

2. Create a Replicate account: https://replicate.com

## Local Testing
```bash
# Test the model locally
cog predict -i prompt="What makes Monad blockchain unique?"

# Build the Docker image
cog build
```

## Push to Replicate
```bash
# Login to Replicate
cog login

# Push the model (replace with your username)
cog push r8.im/YOUR_USERNAME/monad-mistral-7b
```

## Model Structure
- `cog.yaml`: Defines environment and dependencies
- `predict.py`: Contains the Predictor class for inference
- `monad-mistral-7b.gguf`: The model file (will be uploaded separately)

## Using the Model on Replicate

Once deployed, you can use it via:

### Python
```python
import replicate

output = replicate.run(
    "YOUR_USERNAME/monad-mistral-7b:latest",
    input={
        "prompt": "Explain Monad's parallel execution",
        "temperature": 0.7,
        "max_tokens": 200
    }
)
print(output)
```

### cURL
```bash
curl -s -X POST \
  -H "Authorization: Token $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "version": "latest",
    "input": {
      "prompt": "What is Monad?"
    }
  }' \
  https://api.replicate.com/v1/predictions
```

## Notes
- The GGUF file needs to be included in the model package
- Replicate will automatically handle GPU allocation
- The model uses llama-cpp-python for efficient GGUF inference
- Context window is set to 4096 tokens