mirror of
https://github.com/NousResearch/hermes-agent.git
synced 2026-04-26 01:01:40 +00:00
732c66b0f3
The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
70 lines
2.1 KiB
Markdown
70 lines
2.1 KiB
Markdown
# SAELens Reference Documentation
|
||
|
||
This directory contains comprehensive reference materials for SAELens.
|
||
|
||
## Contents
|
||
|
||
- [api.md](api.md) - Complete API reference for SAE, TrainingSAE, and configuration classes
|
||
- [tutorials.md](tutorials.md) - Step-by-step tutorials for training and analyzing SAEs
|
||
- [papers.md](papers.md) - Key research papers on sparse autoencoders
|
||
|
||
## Quick Links
|
||
|
||
- **GitHub Repository**: https://github.com/jbloomAus/SAELens
|
||
- **Neuronpedia**: https://neuronpedia.org (browse pre-trained SAE features)
|
||
- **HuggingFace SAEs**: Search for tag `saelens`
|
||
|
||
## Installation
|
||
|
||
```bash
|
||
pip install sae-lens
|
||
```
|
||
|
||
Requirements: Python 3.10+, transformer-lens>=2.0.0
|
||
|
||
## Basic Usage
|
||
|
||
```python
|
||
from transformer_lens import HookedTransformer
|
||
from sae_lens import SAE
|
||
|
||
# Load model and SAE
|
||
model = HookedTransformer.from_pretrained("gpt2-small", device="cuda")
|
||
sae, cfg_dict, sparsity = SAE.from_pretrained(
|
||
release="gpt2-small-res-jb",
|
||
sae_id="blocks.8.hook_resid_pre",
|
||
device="cuda"
|
||
)
|
||
|
||
# Encode activations to sparse features
|
||
tokens = model.to_tokens("Hello world")
|
||
_, cache = model.run_with_cache(tokens)
|
||
activations = cache["resid_pre", 8]
|
||
|
||
features = sae.encode(activations) # Sparse feature activations
|
||
reconstructed = sae.decode(features) # Reconstructed activations
|
||
```
|
||
|
||
## Key Concepts
|
||
|
||
### Sparse Autoencoders
|
||
SAEs decompose dense neural activations into sparse, interpretable features:
|
||
- **Encoder**: Maps d_model → d_sae (typically 4-16x expansion)
|
||
- **ReLU/TopK**: Enforces sparsity
|
||
- **Decoder**: Reconstructs original activations
|
||
|
||
### Training Loss
|
||
`Loss = MSE(original, reconstructed) + L1_coefficient × L1(features)`
|
||
|
||
### Key Metrics
|
||
- **L0**: Average number of active features (target: 50-200)
|
||
- **CE Loss Score**: Cross-entropy recovered vs original model (target: 80-95%)
|
||
- **Dead Features**: Features that never activate (target: <5%)
|
||
|
||
## Available Pre-trained SAEs
|
||
|
||
| Release | Model | Description |
|
||
|---------|-------|-------------|
|
||
| `gpt2-small-res-jb` | GPT-2 Small | Residual stream SAEs |
|
||
| `gemma-2b-res` | Gemma 2B | Residual stream SAEs |
|
||
| Various | Search HuggingFace | Community-trained SAEs |
|