Comments (4)
Apologies. I'm currently working on resaving the old SAE pickle files in a new format that gets us off the backward compatibility issues. For now, installing eindex should get this to work.
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ahh this is so annoying. It's the old artefacts we need to load which apparently need to be compatible with the old library. I'll attempt a fix today.
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Similar issues with logits_lens_with_features.ipynb
(Error loading the state dictionary from .pt file: No module named 'sae_training') and evaluating_your_sae.ipynb
(No module named 'sae_lens.analysis.visualizer')
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Not creating a pull request due to possibility that the correct fix is for eindex to be a dependency in sae_vis, not SAELens.
It is and it seems to have been patched on GitHub but not pushed yet to PyPI
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Related Issues (20)
- [Bug Report] SAE.from_pretrained errors out in Hooked_SAE_Transformer_Demo.ipynb HOT 2
- [Bug Report] Log sparsity artifact seems wrong HOT 2
- [Proposal] Implement OpenAI's TopK Auxilliary for preventing dead latents.
- [Bug Report] When installing sae_lens get warning that "cannot import name 'deprecated' from 'typing_extensions'" when importing HOT 1
- [Proposal] Support Remaining GPT2 Small OpenAI SAEs in SAE Lens
- [Bug Report] Not able to load pretrained SAE using gated architecture HOT 2
- [Proposal] Make SAE Lens compatible with the latest Transformers / TransformerLens versions
- [Bug Report] load_from_pretrained should respect device parameter
- [Proposal] Save norm scaling factor HOT 1
- [Bug Report] Model saving fails on models with `/` in the name
- [Bug Report] Fine-tuning SAEs with from_pretrained_path seems broken
- [Bug Report] Backwards hooks broken in v2.x.x HOT 1
- [Bug Report] read_sae_from_disk should default to the SAE dtype unless it's overridden
- [Proposal] Add use_error_term as an optional param for HookedSAETransformer.add_sae HOT 1
- [Bug Report] hook_sae_input should not be on x - self.b_dec, it should be on x HOT 1
- [Proposal] Add a custom SAE.to method to update sae.dtype and sae.cfg.dtype when running sae.to(torch.float32)
- [Proposal] Download tracking, code snippets, and filtering on the Hugging Face Hub HOT 5
- [Proposal] Use stop_at_layer when running with cache in evals
- [Bug Report] sae-lens and umap-learn compatibility issue HOT 4
- [Question] Clarification on Feature Annotation in SAEs HOT 1
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