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This commit addresses issue #17 by providing a comprehensive review of the TurboQuant initiative and implementing key improvements. ## Changes ### 1. Initiative Review (docs/INITIATIVE_REVIEW.md) - Comprehensive assessment of current state - Code quality findings and recommendations - Contributor feedback for @manus, @Timmy, @Rockachopa - Implementation plan with clear milestones ### 2. Code Improvements #### llama-turbo.cpp - Added input validation with assertions - Optimized Lloyd-Max search with binary search (O(log n) vs O(n)) - Added stack allocation for d=128 (avoids heap allocation in hot path) - Added error handling for edge cases - Added decision boundaries for efficient quantization #### ggml-metal-turbo.metal - Added bounds checking to all kernels - Added NaN/Inf handling for numerical stability - Completed fused attention kernel (was stub) - Added fused attention with softmax kernel - Added Metal encoding kernel for completeness - Added binary search for quantization ### 3. Testing (tests/test_turbo.cpp) - Unit tests for encode/decode round-trip - Tests for known values (zeros, ones) - Tests for edge cases (large/small values) - Error handling tests ### 4. Build System (CMakeLists.txt) - Added CMake configuration for building library - Added test executable - Added install targets ### 5. Documentation (README.md) - Added build instructions - Added API documentation - Added contributing guidelines - Added code style guide ## Key Improvements 1. **Performance**: Binary search instead of linear search for Lloyd-Max quantization 2. **Memory**: Stack allocation for common case (d=128) 3. **Reliability**: Input validation and error handling 4. **Metal Integration**: Complete fused attention implementation 5. **Testing**: Unit tests for correctness verification 6. **Documentation**: Contributor guidelines and API docs ## Next Steps 1. Run benchmarks to verify performance improvements 2. Test with actual models (qwen3.5:27b) 3. Integrate with llama.cpp fork 4. Deploy to production Closes #17