vdlad/Remarkable-Robustness-of-LLMs
Codebase the paper "The Remarkable Robustness of LLMs: Stages of Inference?"
This codebase allows AI researchers to conduct in-depth analyses of Large Language Models (LLMs) to understand their internal workings and robustness. It takes a pre-trained LLM and various experimental configurations as input, then outputs dataframes, visualizations, and metrics detailing neuron activity, attention patterns, and model behavior under interventions. Researchers and alignment engineers who study model interpretability and reliability would use this.
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Use this if you need to perform detailed ablation studies, analyze neuron activations, or visualize attention mechanisms within TransformerLens-supported LLMs to understand their decision-making processes.
Not ideal if you are a practitioner solely focused on deploying or fine-tuning LLMs without needing to delve into their internal, mechanistic interpretability.
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Language
Jupyter Notebook
License
MIT
Last pushed
Jun 11, 2025
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