BitNet-7B-KDE: Revolutionizing AI Model Training with Knowledge Distillation and Ternary Weights

BitNet-7B-KDE: A Practical Guide for Understanding and Hands-on Exploration Table of Contents Introduction 1. Core Idea of BitNet-7B-KDE 2. Key Technical Concepts Explained 1. Top-K + Other 2. Tokenizer Projection and Deduplication 3. Ternary Weights 4. Activation Flip (A8 → A4) 5. Combined Loss Functions 6. Numerical Safety Mechanisms 3. Environment Setup and .env Explained 4. Core Tasks and Workflow 5. KD Traces Data Structure 6. Loss Function Logic 7. Dry-run Memory Validation 8. Common Issues and Solutions 9. Evaluation Metrics and Reports 10. Code Structure Breakdown 11. Practical Tips for Running 12. Step-by-Step Runbook 13. Conclusion Introduction As AI …