# nnsight-remote-interpretability

Guides neural network interpretability and intervention using nnsight locally or remotely via NDIF. Useful for experimenting with 70B+ models without local GPU clusters.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 945 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/nnsight/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/nnsight-remote-interpretability

## What it does

The skill guides mechanistic interpretability workflows using the `nnsight` library on PyTorch models. Everything runs inside a `trace` context manager, which collects tensor operations into a computation graph using Proxy objects. Activations are retained outside the context with `.save()`. Activation patching works by direct slice assignment. The core syntax stays identical whether running GPT-2 locally or scaling to Llama-3.1-405B via `remote=True`.

Running experiments requires `nnsight>=0.5.0` and `torch>=2.0.0`, with optional `nnsight[vllm]` for vLLM support. Remote execution on NDIF hardware strictly requires an API key from login.ndif.us — without that credential, remote tracing fails. If your project focuses on training SAEs or requires unified model wrappers, the documentation suggests using SAELens or TransformerLens instead.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill nnsight-remote-interpretability -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill nnsight -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/nnsight/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/nnsight/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill nnsight -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/nnsight/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill nnsight -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/nnsight/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs nnsight
```

2. **Manually: put the files in place**
- File: `.github/skills/nnsight/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill nnsight -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 04-mechanistic-interpretability/nnsight
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/nnsight/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill nnsight -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/nnsight/SKILL.md

## When to use it
- extract layer activations and attention patterns from PyTorch models
- run activation patching experiments across clean and corrupted prompts
- execute interpretability traces on 70B+ models remotely using NDIF
- intervene on hidden states during generation without rewriting model code

## Example prompts
- Help me set up activation patching on gpt2 using nnsight
- Write code to capture layer activations with LanguageModel and model.trace using .save()
- Show how to dispatch an interpretability trace to the remote NDIF cluster with remote=True

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 0b338bd63ca2df38.

BEGIN THIRD-PARTY FILE 0b338bd63ca2df38
````text
---
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()

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

## When to Use nnsight

**Use nnsight when you need to:**
- Run interpretability experiments on models too large for local GPUs (70B, 405B)
- Work with any PyTorch architecture (transformers, Mamba, custom models)
- Perform multi-token generation interventions
- Share activations between different prompts
- Access full model internals without reimplementation

**Consider alternatives when:**
- You want consistent API across models → Use **TransformerLens**
- You need declarative, shareable interventions → Use **pyvene**
- You're training SAEs → Use **SAELens**
- You only work with small models locally → **TransformerLens** may be simpler

## Installation

```bash
# Basic installation
pip install nnsight

# For vLLM support
pip install "nnsight[vllm]"
```

For remote NDIF execution, sign up at [login.ndif.us](https://login.ndif.us) for an API key.

## Core Concepts

### LanguageModel Wrapper

```python
from nnsight import LanguageModel

# Load model (uses HuggingFace under the hood)
````
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