nnsight-remote-interpretability

Activation patching in neural networks replaces or modifies internal layer states to test causal hypotheses and steer model behavior. You can patch clean activations into a corrupted prompt to observe prediction changes and verify causal mechanisms.

You can run interpretability workflows on very large models without local GPU capacity through remote NDIF execution. This enables the same tracing code used for small local models to execute seamlessly on massive remote models like a 70B parameter architecture.

To compare activations across multiple prompts in a single session, open a trace context that supports multi-prompt experiments. This allows you to capture and transfer intermediates across different inputs within the same tracing workflow to analyze behavioral differences.

You do not need to rewrite PyTorch model code to perform gradient-based analysis. The tool intercepts and manipulates neural network internals directly, letting you capture gradients and inspect outputs without modifying the original model architecture.

Activation patching in mechanistic interpretability is used to replace or modify internal layer states to test causal hypotheses and steer model behavior. You can patch clean activations into a corrupted prompt to observe prediction changes and verify causal mechanisms.