Скачать или смотреть The Dark Matter of AI [Mechanistic Interpretability]

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Juan Benet, Ross Hanson, Yan Babitski, AJ Englehardt, Alvin Khaled, Eduardo Barraza, Hitoshi Yamauchi, Jaewon Jung, Mrgoodlight, Shinichi Hayashi, Sid Sarasvati, Dominic Beaumont, Shannon Prater, Ubiquity Ventures, Matias Forti, Brian Henry, Tim Palade, Petar Vecutin, Nicolas baumann, Jason Singh, Robert Riley, vornska, Barry Silverman

My Gemma walkthrough notebook: https://colab.research.google.com/dri...
Most animations made with Manim: https://github.com/3b1b/manim

References and Further Reading
Chris Olah’s original “Dark Matter of Neural Networks” post: https://transformer-circuits.pub/2024...
Great recent interview with Chris Olah:    • Dario Amodei: Anthropic CEO on Claude, AGI...  
Gemma Scope: https://arxiv.org/pdf/2408.05147
Experiment with SAEs yourself here! https://www.neuronpedia.org/
Relevant work from the Anthropic team:
https://transformer-circuits.pub/2022...
https://transformer-circuits.pub/2023...
https://transformer-circuits.pub/2024...
Excellent intro Mechanistic Interpretability: https://arena3-chapter1-transformer-i...
Neel Nanda’s Mechanistic Interpretability Explainer: https://dynalist.io/d/n2ZWtnoYHrU1s4v...
Transformer Lens: https://github.com/TransformerLensOrg...
SAE Lens: https://jbloomaus.github.io/SAELens/

Technical Notes
1. There are more advanced and more meaningful ways to map mid layer vectors to outputs, see: https://arxiv.org/pdf/2303.08112, https://neuralblog.github.io/logit-pr..., https://www.lesswrong.com/posts/AcKRB...
2. The 6x2304 matrix is actually 7x2304, we’re ignoring the /bos token.
3. Gemma also includes positional embeddings and lots and lots of normalization layers, which we didn’t really cover
4. I’m conflating tokens and words sometimes, in this example each word is a token, so we don’t have to worry about it too much
5. The “_” characters represent spaces in the token strings