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- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities. - Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools. - Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing. - Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format. - Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5. - Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills. - Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.
70 lines
2.1 KiB
Markdown
70 lines
2.1 KiB
Markdown
# SAELens Reference Documentation
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This directory contains comprehensive reference materials for SAELens.
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## Contents
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- [api.md](api.md) - Complete API reference for SAE, TrainingSAE, and configuration classes
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- [tutorials.md](tutorials.md) - Step-by-step tutorials for training and analyzing SAEs
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- [papers.md](papers.md) - Key research papers on sparse autoencoders
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## Quick Links
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- **GitHub Repository**: https://github.com/jbloomAus/SAELens
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- **Neuronpedia**: https://neuronpedia.org (browse pre-trained SAE features)
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- **HuggingFace SAEs**: Search for tag `saelens`
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## Installation
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```bash
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pip install sae-lens
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```
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Requirements: Python 3.10+, transformer-lens>=2.0.0
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## Basic Usage
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```python
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from transformer_lens import HookedTransformer
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from sae_lens import SAE
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# Load model and SAE
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model = HookedTransformer.from_pretrained("gpt2-small", device="cuda")
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sae, cfg_dict, sparsity = SAE.from_pretrained(
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release="gpt2-small-res-jb",
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sae_id="blocks.8.hook_resid_pre",
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device="cuda"
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)
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# Encode activations to sparse features
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tokens = model.to_tokens("Hello world")
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_, cache = model.run_with_cache(tokens)
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activations = cache["resid_pre", 8]
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features = sae.encode(activations) # Sparse feature activations
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reconstructed = sae.decode(features) # Reconstructed activations
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```
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## Key Concepts
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### Sparse Autoencoders
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SAEs decompose dense neural activations into sparse, interpretable features:
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- **Encoder**: Maps d_model → d_sae (typically 4-16x expansion)
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- **ReLU/TopK**: Enforces sparsity
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- **Decoder**: Reconstructs original activations
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### Training Loss
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`Loss = MSE(original, reconstructed) + L1_coefficient × L1(features)`
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### Key Metrics
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- **L0**: Average number of active features (target: 50-200)
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- **CE Loss Score**: Cross-entropy recovered vs original model (target: 80-95%)
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- **Dead Features**: Features that never activate (target: <5%)
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## Available Pre-trained SAEs
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| Release | Model | Description |
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|---------|-------|-------------|
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| `gpt2-small-res-jb` | GPT-2 Small | Residual stream SAEs |
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| `gemma-2b-res` | Gemma 2B | Residual stream SAEs |
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| Various | Search HuggingFace | Community-trained SAEs |
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