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--- |
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license: mit |
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datasets: |
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- togethercomputer/RedPajama-Data-V2 |
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language: |
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- en |
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library_name: transformers |
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--- |
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This is a set of sparse autoencoders (SAEs) trained on [Llama 3.1 8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) using the 10B sample of the [RedPajama v2 corpus](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2), which comes out to roughly 8.5B tokens using the Llama 3 tokenizer. The SAEs are organized by hookpoint, and can be loaded using the EleutherAI [`sae` library](https://github.com/EleutherAI/sae). |
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With the `sae` library installed, you can access an SAE like this: |
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```python |
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from sae import Sae |
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sae = Sae.load_from_hub("EleutherAI/sae-llama-3.1-8b-32x", hookpoint="layers.23.mlp") |
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``` |
