Nnsight Remote Interpretability

orchestra-research/ai-research-skills

This gives Claude the ability to help with nnsight, a PyTorch interpretability library that lets you access and modify neural network internals with the same code running locally on GPT-2 or remotely on 70B+ models via NDIF. The write-once-run-anywhere pattern is genuinely useful if you're prototyping interventions on small models before scaling up. It works with any PyTorch architecture, not just transformers, and handles multi-token generation interventions cleanly. The remote execution through NDIF is the main differentiator here, letting you run activation patching experiments on massive models without GPU access. If you only work with small models locally, TransformerLens is probably simpler, but for researchers who need to scale interpretability work to frontier models, this is purpose-built for that.

skills.sh installs

1K

GitHub stars

14.4K

last updated

Aug 21, 2026

# Local execution (small model)
with model.trace("Hello world"):
    hidden = model.transformer.h[5].output[0].save()

# Remote execution (massive model) - same code!
with model.trace("Hello world", remote=True):
    hidden = model.model.layers[40].output[0].save()

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