- 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

…imization## OverviewThis PR adds comprehensive support for running Stagehand with local Hugging Face models, enabling on-premises web automation without cloud dependencies. The implementation includes critical fixes for GPU memory management, JSON parsing, and empty result handling.## Key Features- **Local LLM Integration**: Full support for Hugging Face transformers with 4-bit quantization (~7GB VRAM)- **GPU Memory Optimization**: Prevents memory leaks by using shared model instances across multiple operations- **Robust JSON Extraction**: 5-strategy parsing pipeline with intelligent fallbacks for structured data- **Content Preservation**: Never loses content - wraps unparseable output in valid JSON structures- **Graceful Error Handling**: Comprehensive fallback mechanisms prevent empty results## Technical Improvements### 1. GPU Memory Management (examples/example_huggingface.py)- Removed model_name from StagehandConfig to prevent duplicate model loading- Implemented shared global model instance pattern- Added cleanup() between examples and full_cleanup() at program end- Result: Memory stays at ~7GB instead of accumulating to 23GB+### 2. Enhanced JSON Parsing (stagehand/llm/huggingface_client.py)- 5-strategy extraction pipeline:  1. Direct JSON parsing  2. Pattern matching for extraction fields  3. Markdown code block extraction  4. Flexible JSON object detection  5. Natural language to JSON conversion- Aggressive prompt engineering for JSON-only output- Input truncation to prevent CUDA OOM errors- Fallback responses when model unavailable### 3. Content Preservation (stagehand/llm/inference.py)- Critical fix: Wrap raw content in {"extraction": ...} on JSON parse failure- Prevents content loss during parsing errors- Ensures no empty results### 4. Lenient Schema Validation (stagehand/handlers/extract_handler.py)- Three-tier validation with fallbacks- Key normalization (camelCase ↔ snake_case)- Extracts any available string content for DefaultExtractSchema- Creates valid instances even from malformed data## Files Modified- examples/example_huggingface.py: Global model instance pattern- stagehand/llm/huggingface_client.py: Enhanced JSON parsing and memory management- stagehand/llm/inference.py: Content preservation on parse failures- stagehand/handlers/extract_handler.py: Lenient validation with fallbacks- stagehand/schemas.py: Schema compatibility improvements## TestingAll 7 examples run successfully:✅ Basic extraction✅ Data analysis✅ Content generation✅ Multi-step workflow✅ Dynamic content✅ Structured extraction✅ Complex multi-page workflow## Performance- Memory: ~7GB VRAM (with 4-bit quantization)- No CUDA OOM errors- Zero empty results- Graceful degradation on errors## DocumentationExisting HUGGINGFACE_SUPPORT.md provides comprehensive usage guide.Fixes issues with GPU memory exhaustion, empty extraction results, and JSON parsing failures in local model inference.