neelnanda-io / 1L-Sparse-Autoencoder
☆116Updated last year
Alternatives and similar repositories for 1L-Sparse-Autoencoder:
Users that are interested in 1L-Sparse-Autoencoder are comparing it to the libraries listed below
- ☆203Updated 4 months ago
- Create feature-centric and prompt-centric visualizations for sparse autoencoders (like those from Anthropic's published research).☆182Updated 2 months ago
- ☆151Updated this week
- Using sparse coding to find distributed representations used by neural networks.☆213Updated last year
- ☆55Updated 3 months ago
- ☆243Updated last week
- Mechanistic Interpretability Visualizations using React☆232Updated 2 months ago
- ☆109Updated 6 months ago
- Sparse Autoencoder for Mechanistic Interpretability☆216Updated 7 months ago
- Steering Llama 2 with Contrastive Activation Addition☆123Updated 8 months ago
- A library for efficient patching and automatic circuit discovery.☆53Updated this week
- ☆52Updated this week
- ☆142Updated 3 weeks ago
- Steering vectors for transformer language models in Pytorch / Huggingface☆88Updated this week
- Open source replication of Anthropic's Crosscoders for Model Diffing☆39Updated 3 months ago
- Sparse Autoencoder Training Library☆41Updated 3 months ago
- ☆86Updated last week
- Sparse probing paper full code.☆53Updated last year
- Notebooks accompanying Anthropic's "Toy Models of Superposition" paper☆112Updated 2 years ago
- ☆25Updated 10 months ago
- Code for my NeurIPS 2024 ATTRIB paper titled "Attribution Patching Outperforms Automated Circuit Discovery"☆29Updated 8 months ago
- ☆29Updated this week
- ☆25Updated 3 months ago
- Sparsify transformers with SAEs and transcoders☆461Updated this week
- ☆76Updated 6 months ago
- How do transformer LMs encode relations?☆46Updated 11 months ago
- Algebraic value editing in pretrained language models☆62Updated last year
- Tools for studying developmental interpretability in neural networks.☆84Updated 3 weeks ago
- Mechanistic Interpretability for Transformer Models☆49Updated 2 years ago
- The nnsight package enables interpreting and manipulating the internals of deep learned models.☆490Updated this week