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| # Active Context: Morris Bot Current State | |
| ## Current Work Focus | |
| ### Project Status: Phase 2 Enhanced Model Complete ✅ | |
| The Morris Bot has successfully completed Phase 2 with an enhanced model that significantly improves Iain Morris's distinctive writing style. The enhanced model includes better system prompts, expanded training data, and improved style capture. | |
| ### Recent Major Achievements | |
| - **Enhanced Model Training**: New `iain-morris-model-enhanced` with improved style capture | |
| - **Comprehensive System Prompt**: Detailed style guide with doom-laden openings, cynical wit, and signature phrases | |
| - **Expanded Training Data**: 126 examples (up from 118) including non-telecom topics | |
| - **Updated Gradio App**: Now uses enhanced model with Apple Silicon MPS optimization | |
| - **Improved Training Parameters**: 4 epochs, reduced learning rate (5e-5), better style learning | |
| - **Multi-topic Capability**: Can generate Morris-style content beyond just telecom | |
| ### Current Capabilities | |
| - ✅ **Content Generation**: Produces coherent, well-structured articles | |
| - ✅ **Technical Accuracy**: Correct telecom industry knowledge and terminology | |
| - ✅ **Fast Inference**: 2-5 seconds per article on Apple Silicon | |
| - ✅ **Memory Efficiency**: Operates within 8GB RAM using LoRA | |
| - ✅ **User Interface**: Simple web interface for topic input and generation | |
| ## Next Steps (Immediate Priorities) | |
| ### Priority 1: Enhanced Model Testing & Validation 🎯 | |
| **Current Status**: Enhanced model deployed and running in Gradio app | |
| **Immediate Actions**: | |
| 1. **Comprehensive Testing**: Test enhanced model across diverse topics | |
| - Validate improved cynical tone and doom-laden openings | |
| - Test non-telecom topics (dating, work, social media, health) | |
| - Compare outputs with original model for style improvements | |
| 2. **Style Accuracy Assessment**: Evaluate if 90%+ style target achieved | |
| - Test signature phrases ("What could possibly go wrong?") | |
| - Validate dark analogies and visceral metaphors | |
| - Assess British cynicism and parenthetical snark | |
| 3. **Performance Validation**: Ensure enhanced model maintains performance | |
| - Verify 2-5 second generation times on Apple Silicon | |
| - Monitor memory usage and stability | |
| - Test various generation parameters | |
| ### Priority 2: User Experience Refinement | |
| **Current State**: Enhanced Gradio app functional with improved model | |
| **Planned Improvements**: | |
| - Add non-telecom example topics to showcase versatility | |
| - Improve UI styling and user feedback | |
| - Add model comparison features (original vs enhanced) | |
| - Better parameter controls for generation settings | |
| ### Priority 3: Documentation & Deployment | |
| **Current State**: Enhanced model working, documentation needs updating | |
| **Required Updates**: | |
| - Update README with enhanced model capabilities | |
| - Document new system prompt structure and style elements | |
| - Create user guide for enhanced features | |
| - Prepare deployment documentation | |
| ## Active Decisions and Considerations | |
| ### Model Architecture Decisions | |
| - **Staying with Zephyr-7B-Beta**: Proven to work well, no need to change base model | |
| - **LoRA Approach**: Confirmed as optimal for hardware constraints and training efficiency | |
| - **Apple Silicon Focus**: Continue optimizing for M1/M2/M3 as primary target platform | |
| ### Training Strategy Decisions | |
| - **Conservative Approach**: Prefer stable training over aggressive optimization | |
| - **Quality over Quantity**: Focus on high-quality training examples rather than volume | |
| - **Iterative Improvement**: Small, measurable improvements rather than major overhauls | |
| ### Development Workflow Decisions | |
| - **Memory Bank Documentation**: Maintain comprehensive documentation for context continuity | |
| - **Modular Architecture**: Keep components separate for easier testing and improvement | |
| - **Validation-First**: Always validate changes with test scripts before deployment | |
| ## Important Patterns and Preferences | |
| ### Code Organization Patterns | |
| - **Separation of Concerns**: Keep data processing, training, and inference separate | |
| - **Configuration Centralization**: All training parameters in one place | |
| - **Error Handling**: Comprehensive logging and graceful degradation | |
| - **Hardware Abstraction**: Automatic device detection with fallbacks | |
| ### Development Preferences | |
| - **Apple Silicon Optimization**: Primary development and testing platform | |
| - **Memory Efficiency**: Always consider RAM usage in design decisions | |
| - **User Experience**: Prioritize simplicity and responsiveness | |
| - **Documentation**: Maintain clear, comprehensive documentation | |
| ### Quality Standards | |
| - **Technical Accuracy**: Generated content must be factually correct | |
| - **Style Consistency**: Aim for recognizable Iain Morris voice | |
| - **Performance**: Sub-5-second generation times | |
| - **Reliability**: Stable operation without crashes or memory issues | |
| ## Learnings and Project Insights | |
| ### What Works Well | |
| 1. **LoRA Fine-tuning**: Extremely effective for style transfer with limited resources | |
| 2. **Apple Silicon MPS**: Provides excellent performance for ML workloads | |
| 3. **Gradio Interface**: Rapid prototyping and user testing | |
| 4. **Modular Architecture**: Easy to test and improve individual components | |
| 5. **Conservative Training**: Stable convergence without overfitting | |
| ### Key Challenges Identified | |
| 1. **Style Authenticity**: Capturing distinctive voice requires more training data | |
| 2. **Dataset Size**: 18 examples insufficient for complex style learning | |
| 3. **Topic Diversity**: Need broader range of topics to capture full writing style | |
| 4. **Evaluation Metrics**: Difficult to quantify "style accuracy" objectively | |
| ### Technical Insights | |
| 1. **Memory Management**: LoRA enables training large models on consumer hardware | |
| 2. **Hardware Optimization**: MPS backend crucial for Apple Silicon performance | |
| 3. **Training Stability**: Conservative learning rates prevent instability | |
| 4. **Model Loading**: Lazy loading improves user experience | |
| ### Process Insights | |
| 1. **Documentation Value**: Memory bank crucial for maintaining context | |
| 2. **Iterative Development**: Small improvements compound effectively | |
| 3. **Validation Importance**: Test scripts catch issues early | |
| 4. **User Feedback**: Simple interface enables rapid testing and feedback | |
| ## Current Technical State | |
| ### Model Files Status | |
| - **Base Model**: Zephyr-7B-Beta cached locally | |
| - **Enhanced Model**: `models/iain-morris-model-enhanced/` - Primary model in use | |
| - **Original Model**: `models/lora_adapters/` - Legacy model for comparison | |
| - **Checkpoints**: Multiple training checkpoints (50, 100, 104) available | |
| - **Tokenizer**: Properly configured and saved with enhanced model | |
| ### Data Pipeline Status | |
| - **Enhanced Dataset**: 126 examples in `data/enhanced_train_dataset.json` (current) | |
| - **Improved Dataset**: 119 examples in `data/improved_train_dataset.json` | |
| - **Original Dataset**: 18 examples in `data/train_dataset.json` (legacy) | |
| - **Validation Data**: Enhanced validation set in `data/improved_val_dataset.json` | |
| - **HuggingFace Datasets**: Cached for efficient retraining | |
| ### Application Status | |
| - **Web Interface**: Enhanced Gradio app in `app.py` using improved model | |
| - **Model Testing**: Multiple test scripts available (`test_enhanced_model.py`, `test_enhanced_style.py`) | |
| - **Pipeline Scripts**: Full automation available via `run_pipeline.py` | |
| - **Enhancement Tools**: `update_system_prompt.py`, `add_non_telecom_examples.py` | |
| - **Logging**: Comprehensive logging to `morris_bot.log` | |
| - **Current Status**: App running on localhost:7860 with enhanced model loaded | |
| ## Environment and Dependencies | |
| ### Current Environment | |
| - **Python**: 3.8+ with virtual environment | |
| - **Hardware**: Optimized for Apple Silicon M1/M2/M3 | |
| - **Dependencies**: All requirements installed and tested | |
| - **Storage**: ~5GB used for models and data | |
| ### Known Issues | |
| - **None Critical**: System is stable and functional | |
| - **Style Limitation**: Primary area for improvement identified | |
| - **Dataset Size**: Expansion needed for better results | |
| ## Next Session Priorities | |
| When resuming work on this project: | |
| 1. **Read Memory Bank**: Review all memory bank files for full context | |
| 2. **Test Current State**: Run `python test_finetuned_model.py` to verify functionality | |
| 3. **Check Improvement Guide**: Review `improve_training_guide.md` for detailed next steps | |
| 4. **Focus on Style Enhancement**: Priority 1 is expanding training data for better style capture | |
| 5. **Validate Changes**: Always test improvements before considering them complete | |
| ## Success Metrics Tracking | |
| ### Current Performance | |
| - **Training Loss**: 1.988 (excellent) | |
| - **Generation Speed**: 2-5 seconds (target met) | |
| - **Memory Usage**: ~8GB (within constraints) | |
| - **Style Accuracy**: ~70% (needs improvement to 90%+) | |
| - **Technical Accuracy**: High (telecom knowledge captured well) | |
| ### Improvement Targets | |
| - **Style Accuracy**: 70% → 90%+ | |
| - **Training Data**: 18 → 100+ examples | |
| - **Topic Coverage**: Telecom only → Multi-topic | |
| - **User Experience**: Basic → Enhanced with better controls | |