tim-lawson
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Push model using huggingface_hub.
Browse files- README.md +47 -0
- config.json +20 -0
- model.safetensors +3 -0
README.md
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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-410m-deduped-x64-k32-tfm-layers-22
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A Multi-Layer Sparse Autoencoder (MLSAE) trained on the residual stream activation
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vectors from [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-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 Lightning MLSAETransformer module, which includes 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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```
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config.json
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{
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"accumulate_grad_batches": 64,
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"auxk": 256,
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"auxk_coef": 0.03125,
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"batch_size": 1,
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"dead_steps_threshold": null,
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"dead_threshold": 0.001,
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"dead_tokens_threshold": 10000000,
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"expansion_factor": 64,
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"k": 32,
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"layers": [
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22
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],
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"lr": 0.0001,
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"max_length": 2048,
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"model_name": "EleutherAI/pythia-410m-deduped",
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"skip_special_tokens": true,
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"standardize": true,
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"tuned_lens": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c775fb9fbb0523aebac43f19442905aad71485b39fb158be70376284cf3449ef
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size 2158251016
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