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--- |
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language: en |
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library_name: mlsae |
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license: mit |
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tags: |
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- arxiv:2409.04185 |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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--- |
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# Model Card for tim-lawson/sae-pythia-70m-deduped-x64-k32-lens-layers-5 |
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A Multi-Layer Sparse Autoencoder (MLSAE) trained on the residual stream activation |
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vectors from [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) with an |
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expansion factor of R = 64 and sparsity k = 32, over 1 billion |
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tokens from [monology/pile-uncopyrighted](https://huggingface.co/datasets/monology/pile-uncopyrighted). |
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This model is a PyTorch TopKSAE module, which does not include the underlying |
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transformer. |
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### Model Sources |
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- **Repository:** <https://github.com/tim-lawson/mlsae> |
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- **Paper:** <https://arxiv.org/abs/2409.04185> |
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- **Weights & Biases:** <https://wandb.ai/timlawson-/mlsae> |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@misc{lawson_residual_2024, |
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title = {Residual {{Stream Analysis}} with {{Multi-Layer SAEs}}}, |
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author = {Lawson, Tim and Farnik, Lucy and Houghton, Conor and Aitchison, Laurence}, |
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year = {2024}, |
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month = oct, |
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number = {arXiv:2409.04185}, |
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eprint = {2409.04185}, |
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primaryclass = {cs}, |
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publisher = {arXiv}, |
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doi = {10.48550/arXiv.2409.04185}, |
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urldate = {2024-10-08}, |
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archiveprefix = {arXiv} |
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} |
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``` |
