1. Introduction
  3. Getting Started
  4. Installation
  5. Quick Start
  6. First Training Loop
  7. Core Concepts
  9. Architecture
  10. Overview
  11. Design Philosophy
  12. Module Organization
  13. Type System
  14. Memory Management
  16. Autograd Engine
  17. What is Automatic Differentiation?
  18. Tape-Based Computation Graphs
  19. Tensor Operations
    1. Matrix Multiplication
    2. Activations (ReLU, GELU, Swish)
    3. Layer Normalization
    4. Attention Mechanism
  21. Backward Pass
  22. Gradient Computation
  23. Finite Difference Validation
  25. Optimizers
  26. Overview
  27. Stochastic Gradient Descent (SGD)
  28. Adam Optimizer
  29. AdamW (Decoupled Weight Decay)
  30. Learning Rate Schedulers
    1. Cosine Annealing
    2. Step Decay
    3. Exponential Decay
  32. Gradient Clipping
  33. SIMD-Accelerated Updates
  34. Optimizer Theory
  36. LoRA (Low-Rank Adaptation)
  37. What is LoRA?
  38. Parameter-Efficient Fine-Tuning
  39. LoRA Layer Architecture
    1. Low-Rank Matrices A and B
    2. Scaling Factor (alpha/rank)
    3. Merge and Unmerge
  41. Target Module Selection
  42. Gradient Flow Isolation
  43. Adapter Persistence
    1. Saving Adapters
    2. Loading Adapters
    3. Sharing Adapters
  46. QLoRA (Quantized LoRA)
  47. Memory-Efficient Fine-Tuning
  48. 4-bit Quantization
    1. Block-Wise Quantization
    2. Scale Factors
    3. Quantization/Dequantization
  50. QLoRA Layer
  51. On-the-Fly Dequantization
  52. Memory Benchmarks
    1. LoRA vs QLoRA Comparison
    2. Transformer Model Benchmarks
    3. Compression Ratios
  54. Trade-offs and Best Practices
  56. Model Merging
  57. Overview
  58. TIES Algorithm
  59. DARE Algorithm
  60. SLERP Algorithm
  61. Multi-Model Ensembles
  62. Merge Best Practices
  64. Pruning
  65. Overview
  66. Pruning Schedules
  67. Calibration
  68. Pipeline Stages
  70. Knowledge Distillation
  71. What is Distillation?
  72. Temperature-Scaled KL Divergence
  73. Multi-Teacher Ensemble
  74. Progressive Layer-Wise
  75. Distillation Loss Functions
  76. Student-Teacher Architecture
  78. Training Loops
  79. Trainer API
  80. Callback System
  81. Train Config
  82. Basic Training Loop
  83. Batching and Data Loading
  84. Loss Functions
  85. Validation and Testing
  86. Checkpointing
  87. Early Stopping
  88. Curriculum Learning
  89. Explainability
  91. Real-Time Monitoring
  92. Overview
  93. Experiment Tracking
  94. Quality Gates (Jidoka)
  95. Metrics Collection
  96. Terminal Dashboard
  97. Drift Detection
  98. Andon Alerting (Jidoka)
  99. Inference Monitoring
  100. Model Lineage
  101. Export Formats
  102. Hansei Reports
  104. Dashboard
  105. Overview
  106. DashboardSource Trait
  107. WASM Bindings
  109. Ecosystem Integration
  110. Overview
  111. Batuta Integration
  112. Realizar GGUF Export
  113. Ruchy Session Bridge
  115. MLOps (Toyota Way)
  116. Overview
  117. Experiment Tracking
  118. Preflight Validation (Jidoka)
  119. Model Registry (Kanban)
  120. Hyperparameter Optimization (Kaizen)
  121. Differential Privacy
  122. GPU Monitoring (Andon)
  123. REST API Server
  124. Cloud Storage
  125. LLM Evaluation
  127. Model I/O
  128. Overview
  129. Save Models
  130. Load Models
  131. Model Metadata
  132. Supported Formats
    1. SafeTensors Format
    2. JSON Format
    3. YAML Format
    4. GGUF Format
  135. Command-Line Interface
  136. CLI Overview
  137. Research Commands
  138. Benchmark Commands
  139. Inspect Command
  140. Audit Command
  141. Monitor Command
  142. Completion Command
  144. Declarative Training
  145. Overview
  146. YAML Mode Training (v1.0)
  147. YAML Examples Catalog
  148. Toyota Way QA Process
  149. YAML Configuration
  150. train_from_yaml Function
  151. Configuration Schema
  152. Optimizer Builders
  153. Model Builders
  155. API Reference
  156. Tensor API
  157. Autograd Operations
  158. Optimizer API
  159. LoRA API
  160. QLoRA API
  161. Configuration System
  162. Error Handling
  164. Model Evaluation (APR-073)
  165. Overview
  166. Classification Metrics
  167. ModelEvaluator & Leaderboard
  168. Cross-Validation
  169. Drift Detection
    1. KS Test
    2. Chi-Square Test
    3. PSI (Population Stability Index)
  171. Auto-Retraining (Andon Loop)
  173. Examples
  174. Linear Regression with Autograd
  175. Training a Simple MLP
  176. Fine-Tuning with LoRA
  177. Memory-Efficient QLoRA
  178. Custom Loss Functions
  179. Learning Rate Scheduling
  180. Gradient Clipping
  181. Adapter Sharing
  182. CUDA Backend Configuration
  183. Pruning Pipeline
  184. Drift Detection Simulation
  185. P-Value Calibration Check
  187. Development Guide
  188. Contributing
  189. EXTREME TDD Methodology
  190. Testing Strategy
    1. Unit Tests
    2. Property-Based Tests
    3. Gradient Checking Tests
    4. Mutation Testing
  192. Quality Gates
    1. Pre-Commit Hooks
    2. Continuous Integration
    3. Code Coverage
    4. Clippy Linting
  194. Benchmarking
  195. PMAT Toyota Workflow
  197. Best Practices
  198. Optimizer Selection
  199. Learning Rate Tuning
  200. LoRA Configuration
  201. Memory Optimization
  202. Gradient Stability
  203. Debugging Training Issues
  204. Performance Profiling
  206. Search Algorithms
  207. Overview
  208. Monte Carlo Tree Search (MCTS)
    1. State and Action Spaces
    2. UCB1/PUCT Selection
    3. Expansion and Simulation
    4. Backpropagation
  210. Policy Network Integration
  212. Generative Models
  213. Overview
  214. Code Generation GANs
    1. Generator Architecture
    2. Discriminator Architecture
    3. Latent Space Interpolation
    4. Training Loop
  216. Mode Collapse Detection
  218. Advanced Topics
  219. Custom Backward Passes
  220. Implementing New Optimizers
  221. Custom LoRA Variants
  222. Advanced Quantization
  223. Distributed Training
  224. Model Parallelism
  225. Compiler-in-the-Loop (CITL)
  227. Sovereign Deployment
  228. Overview
  230. Academic Research
  231. Overview
  233. Specifications
  234. Autograd Specification
  235. Optimizer Specification
  236. LoRA Specification
  237. Quantization Specification
  238. Academic Foundations
  240. Appendix
  241. Glossary
  242. Mathematical Notation
  243. References
  244. FAQ
  245. Changelog
  246. Migration Guide
  247. Benchmarking Results