Add Hugging Face model support

- Add HuggingFaceLLMClient for local model inference
- Support for 6 popular Hugging Face models (Llama 2, Mistral, Zephyr, etc.)
- Add memory optimization with quantization support
- Create comprehensive example and documentation
- Add unit tests for Hugging Face integration
- Update dependencies to include transformers, torch, accelerate

Author: kmurad-qlu

HUGGINGFACE_SUPPORT.md

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+# Hugging Face Model Support
+
+Stagehand now supports using open-source Hugging Face models for local inference! This allows you to run web automation tasks without relying on external API services.
+
+## Supported Models
+
+The following Hugging Face models are pre-configured and ready to use:
+
+- **Llama 2 7B Chat** (`huggingface/meta-llama/Llama-2-7b-chat-hf`)
+- **Llama 2 13B Chat** (`huggingface/meta-llama/Llama-2-13b-chat-hf`)
+- **Mistral 7B Instruct** (`huggingface/mistralai/Mistral-7B-Instruct-v0.1`)
+- **Zephyr 7B Beta** (`huggingface/HuggingFaceH4/zephyr-7b-beta`)
+- **CodeGen 2.5B Mono** (`huggingface/Salesforce/codegen-2B-mono`)
+- **StarCoder2 7B** (`huggingface/bigcode/starcoder2-7b`)
+
+## Requirements
+
+### Hardware Requirements
+- **GPU**: CUDA-compatible GPU with at least 8GB VRAM (recommended)
+- **RAM**: At least 16GB system RAM
+- **Storage**: 20GB+ free space for model downloads
+
+### Software Requirements
+- Python 3.9+
+- CUDA toolkit (for GPU acceleration)
+- PyTorch with CUDA support
+
+## Installation
+
+Install the required dependencies:
+
+```bash
+pip install transformers torch accelerate bitsandbytes
+```
+
+For GPU support, make sure you have the appropriate CUDA version installed.
+
+## Basic Usage
+
+```python
+import asyncio
+from stagehand import Stagehand, StagehandConfig
+from stagehand.schemas import AvailableModel
+
+async def main():
+    # Configure Stagehand to use a Hugging Face model
+    config = StagehandConfig(
+        env="LOCAL",
+        model_name=AvailableModel.HUGGINGFACE_ZEPHYR_7B,
+        verbose=2,
+        use_api=False,
+    )
+    
+    stagehand = Stagehand(config=config)
+    
+    try:
+        await stagehand.init()
+        await stagehand.navigate("https://example.com")
+        
+        # Extract data using the Hugging Face model
+        result = await stagehand.extract(
+            instruction="Extract the main heading from this page"
+        )
+        
+        print(f"Extracted: {result.data}")
+        
+    finally:
+        await stagehand.close()
+
+asyncio.run(main())
+```
+
+## Advanced Configuration
+
+### Memory Optimization
+
+For systems with limited GPU memory, you can use quantization:
+
+```python
+config = StagehandConfig(
+    env="LOCAL",
+    model_name=AvailableModel.HUGGINGFACE_LLAMA_2_7B,
+    use_api=False,
+    model_client_options={
+        "device": "cuda",
+        "quantization_config": {
+            "load_in_4bit": True,
+            "bnb_4bit_compute_dtype": "float16",
+            "bnb_4bit_use_double_quant": True,
+        }
+    }
+)
+```
+
+### Custom Models
+
+You can also use any Hugging Face model by specifying the full model name:
+
+```python
+config = StagehandConfig(
+    env="LOCAL",
+    model_name="huggingface/your-username/your-model",
+    use_api=False,
+)
+```
+
+## Performance Tips
+
+1. **Use GPU**: Always use CUDA if available for significantly faster inference
+2. **Quantization**: Use 4-bit or 8-bit quantization to reduce memory usage
+3. **Model Size**: Start with smaller models (7B parameters) for testing
+4. **Batch Processing**: Process multiple tasks in sequence rather than parallel
+5. **Memory Management**: Close other GPU applications when running large models
+
+## Troubleshooting
+
+### Out of Memory Errors
+- Use quantization (`load_in_4bit=True`)
+- Try a smaller model
+- Close other GPU applications
+- Use CPU mode (slower but uses less memory)
+
+### Slow Performance
+- Ensure CUDA is properly installed
+- Use GPU instead of CPU
+- Try a smaller model
+- Check if other processes are using GPU
+
+### Model Download Issues
+- Check internet connection
+- Ensure sufficient disk space
+- Try downloading manually from Hugging Face Hub
+
+## Examples
+
+See `examples/example_huggingface.py` for comprehensive examples including:
+- Basic usage with different models
+- Memory-efficient configurations
+- Form filling and data extraction
+- Error handling and troubleshooting
+
+## Limitations
+
+- **First Run**: Models are downloaded on first use (5-15GB)
+- **Memory**: Large models require significant GPU memory
+- **Speed**: Local inference is slower than API calls
+- **Model Quality**: Some models may not perform as well as commercial APIs
+
+## Contributing
+
+To add support for new Hugging Face models:
+
+1. Add the model to `AvailableModel` enum in `schemas.py`
+2. Test the model with various web automation tasks
+3. Update this documentation
+4. Add tests for the new model
+
+## Support
+
+For issues related to Hugging Face model support:
+- Check the [Hugging Face documentation](https://huggingface.co/docs)
+- Review the example file for usage patterns
+- Open an issue on the GitHub repository