Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without loc…
---
name: nnsight-remote-interpretability
description: Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [nnsight, NDIF, Remote Execution, Mechanistic Interpretability, Model Internals]
dependencies: [nnsight>=0.5.0, torch>=2.0.0]
---
# nnsight: Transparent Access to Neural Network Internals
nnsight (/ɛn.saɪt/) enables researchers to interpret and manipulate the internals of any PyTorch model, with the unique capability of running the same code locally on small models or remotely on massive models (70B+) via NDIF.
**GitHub**: [ndif-team/nnsight](https://github.com/ndif-team/nnsight) (730+ stars)
**Paper**: [NNsight and NDIF: Democratizing Access to Foundation Model Internals](https://arxiv.org/abs/2407.14561) (ICLR 2025)
## Key Value Proposition
**Write once, run anywhere**: The same interpretability code works on GPT-2 locally or Llama-3.1-405B remotely. Just toggle `remote=True`.
```python
# Local execution (small model)
with model.trace("Hello world"):
hidden = model.transformer.h[5].output[0].save()… load the full skill through Skill Me