nnsight-remote-interpretability

Activation tracing across large PyTorch models without local GPUs uses nnsight's remote execution via NDIF. You install nnsight, enable NDIF, and use the LanguageModel tracing API to access and save intermediate activations remotely for 70B+ architectures.

Activation patching and cross-prompt sharing are interpretability techniques that manipulate neural network internals. Using nnsight, you can trace a model's execution, extract specific activations, and inject or share them across different prompts to analyze causal mechanisms.

Yes, nnsight applies to any PyTorch architecture for local or remote execution. While NDIF-enabled remote analysis specifically targets 70B+ models and large neural architectures, the LanguageModel tracing API supports activation tracing universally.

To start a tracing session, install nnsight and use the LanguageModel tracing API. You wrap your model in a tracing context to collect intermediate activations, apply interventions like patching, and extract the final logits from the remote or local execution.

Yes, nnsight supports steering experiments on large neural networks. By using the tracing API to access intermediate activations during remote execution, you can manipulate internal states and observe the effects on model outputs across 70B+ parameter architectures.