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Complete API reference for HookedTransformer, ActivationCache, and HookPoints\n- [tutorials.md](tutorials.md) - Step-by-step tutorials for common interpretability workflows\n- [papers.md](papers.md) - Key research papers and foundational concepts\n\n## Quick Links\n\n- **Official Documentation**: https://transformerlensorg.github.io/TransformerLens/\n- **GitHub Repository**: https://github.com/TransformerLensOrg/TransformerLens\n- **Model Properties Table**: https://transformerlensorg.github.io/TransformerLens/generated/model_properties_table.html\n\n## Installation\n\n```bash\npip install transformer-lens\n```\n\n## Basic Usage\n\n```python\nfrom transformer_lens import HookedTransformer\n\n# Load model\nmodel = HookedTransformer.from_pretrained(\"gpt2-small\")\n\n# Run with activation caching\ntokens = model.to_tokens(\"Hello world\")\nlogits, cache = model.run_with_cache(tokens)\n\n# Access activations\nresidual = cache[\"resid_post\", 5]  # Layer 5 residual stream\nattention = cache[\"pattern\", 3]    # Layer 3 attention patterns\n```\n\n## Key Concepts\n\n### HookPoints\nEvery activation in the transformer has a HookPoint wrapper, enabling:\n- Reading activations via `run_with_cache()`\n- Modifying activations via `run_with_hooks()`\n\n### Activation Cache\nThe `ActivationCache` stores all intermediate activations with helper methods for:\n- Residual stream decomposition\n- Logit attribution\n- Layer-wise analysis\n\n### Supported Models (50+)\nGPT-2, LLaMA, Mistral, Pythia, GPT-Neo, OPT, Gemma, Phi, and more.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdicklesworthstone%2Fpi_agent_rust","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdicklesworthstone%2Fpi_agent_rust","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdicklesworthstone%2Fpi_agent_rust/lists"}