nnsight-remote-interpretability

Neural network interpretability on massive LLMs is achieved by running remote execution via NDIF, enabling mechanistic experiments on 70B+ parameter models without requiring local GPU resources.

Mechanistic interpretability experiments can run on any PyTorch architecture, including transformers, Mamba, and custom models, allowing universal activation analysis and gradient-based investigations across diverse network designs.

Activation patching and tracing in large language models involves using a deferred execution API to intervene at critical layers, enabling you to analyze and modify model internals through a clean, traceable interface.

Debugging emergent behavior in a 70B parameter model is best done by tracing activations and intervening at critical layers using remote execution via NDIF, completely avoiding the need for a local supercomputer.

This mechanistic interpretability approach works with any PyTorch architecture, explicitly supporting transformers, Mamba, and custom models for activation analysis, patching, and gradient-based investigations.

Tracing LLM activations does not require local supercomputing hardware because remote execution via NDIF handles the computational load, allowing you to perform mechanistic interpretability experiments on massive 70B+ models remotely.