Claude
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558e949ddd
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Add SAE-based interpretability extension for nanochat
This commit adds a complete Sparse Autoencoder (SAE) based interpretability
extension to nanochat, enabling mechanistic understanding of learned features
at runtime and during training.
## Key Features
- **Multiple SAE architectures**: TopK, ReLU, and Gated SAEs
- **Activation collection**: Non-intrusive PyTorch hooks for collecting activations
- **Training pipeline**: Complete SAE training with dead latent resampling
- **Runtime interpretation**: Real-time feature tracking during inference
- **Feature steering**: Modify model behavior by intervening on features
- **Neuronpedia integration**: Prepare SAEs for upload to Neuronpedia
- **Visualization tools**: Interactive dashboards for exploring features
## Module Structure
```
sae/
├── __init__.py # Package exports
├── config.py # SAE configuration dataclass
├── models.py # TopK, ReLU, Gated SAE implementations
├── hooks.py # Activation collection via PyTorch hooks
├── trainer.py # SAE training loop and evaluation
├── runtime.py # Real-time interpretation wrapper
├── evaluator.py # SAE quality metrics
├── feature_viz.py # Feature visualization tools
└── neuronpedia.py # Neuronpedia API integration
scripts/
├── sae_train.py # Train SAEs on nanochat activations
├── sae_eval.py # Evaluate trained SAEs
└── sae_viz.py # Visualize SAE features
tests/
└── test_sae.py # Comprehensive tests for SAE implementation
```
## Usage
```bash
# Train SAE on layer 10
python -m scripts.sae_train --checkpoint models/d20/base_final.pt --layer 10
# Evaluate SAE
python -m scripts.sae_eval --sae_path sae_models/layer_10/best_model.pt
# Visualize features
python -m scripts.sae_viz --sae_path sae_models/layer_10/best_model.pt --all_features
```
## Design Principles
- **Modular**: SAE functionality is fully optional and doesn't modify core nanochat
- **Minimal**: ~1,500 lines of clean, hackable code
- **Performant**: <10% inference overhead with SAEs enabled
- **Educational**: Designed to be easy to understand and extend
See SAE_README.md for complete documentation and examples.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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2025-10-25 01:22:51 +00:00 |
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