| --- |
| license: mit |
| datasets: |
| - koutch/stackoverflow_python |
| - Vezora/Tested-143k-Python-Alpaca |
| language: |
| - en |
| base_model: |
| - meta-llama/Llama-3.1-8B-Instruct |
| --- |
| |
| This repository contains sparse autoencoders trained to analyze the internal representations of the Llama 3.1 8B Instruct model. The autoencoders are trained on the residual stream activations when processing code-related instruction data. |
|
|
| We apply these specialized, lightweight SAEs on a coding task in our blog post [Sieve](https://tilderesearch.com/blog/sieve). |
|
|
| ## Model Details |
|
|
| - **Model Type:** TopK Sparse Autoencoder |
| - **Base Model:** Llama 3.1 8B Instruct |
| - **Training Data:** 1B tokens of code data from: |
| - StackOverflow Python dataset |
| - Tested-143k Python Alpaca dataset |
| - **Architecture:** Linear encoder-decoder with ReLU and TopK activation (k=64, 512) |
| - **File Format:** PyTorch .pt files containing: |
| - W_enc_DF: Encoder weight matrix |
| - b_enc_F: Encoder bias vector |
| - W_dec_FD: Decoder weight matrix |
| - b_dec_D: Decoder bias vector |
|
|
| ## Usage |
|
|
| The autoencoders can be used to analyze and interpret the internal representations formed by Llama 3.1 8B Instruct when processing code. Since these autoencoders are trained on a very specific sub data mixture, they are not recommended for general purpose. |
| They can be used to reproduce the result of Sieve evaluation for Llama 3.1 8B Instruct. |
|
|
| Example usage can be found in the [Sieve repo](https://github.com/tilde-research/sieve) |
|
|
| ## Training Details |
|
|
| - **Training Data Size:** 1B tokens |
| - **Domain:** Python code and code-related instructions |
| - **Target:** Residual stream activations from Llama 3.1 8B Instruct from layers 8, 10, and 12 |
| - **Compute:** Around 9 A100 hours |
|
|
| ## License |
|
|
| MIT |
|
|
| ## Citation |
|
|
| If you use these models in your research, please cite: |
|
|
| ```bibtex |
| @article{karvonen2024sieve, |
| title={Sieve: SAEs Beat Baselines on a Real-World Task (A Code Generation Case Study)}, |
| author={Karvonen, Adam and Pai, Dhruv and Wang, Mason and Keigwin, Ben}, |
| journal={Tilde Research Blog}, |
| year={2024}, |
| month={12}, |
| url={https://www.tilderesearch.com/blog/sieve}, |
| note={Blog post} |
| } |
