nnsight-remote-interpretability

Activation patching for cross-prompt interventions allows you to study causal effects and model behavior by modifying internal activations across different prompts during a trace. This technique isolates how specific components influence outputs.

Yes, this approach supports custom architectures alongside large transformers for interpretability analysis. Proxy-based access and tracing work across general PyTorch models, enabling structured activation analysis and module path validation for diverse research workflows.

Activation patching for cross-prompt interventions allows you to study causal effects by modifying internal activations across different prompts during a trace. This technique isolates how specific components influence model behavior.

Yes, you need tracing support to analyze model internals because the proxy-based access mechanism operates within a tracing interface. This structured approach ensures internal activations are captured and manipulated safely during execution.

You should avoid remote execution for interpretability analysis when working with small models that fit on local hardware, as local tracing is faster. Remote NDIF execution is designed specifically for massive 70B+ models requiring distributed resources.