/mechanistic-interpretability-nnsight

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`.

# 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

# 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

from nnsight import LanguageModel

# Load model (uses HuggingFace under the hood)
model = LanguageModel("openai-community/gpt2", device_map="auto")

# For larger models
model = LanguageModel("meta-llama/Llama-3.1-8B", device_map="auto")

Tracing Context

The `trace` context manager enables deferred execution - operations are collected into a computation graph:

from nnsight import LanguageModel

model = LanguageModel("gpt2", device_map="auto")

with model.trace("The Eiffel Tower is in") as tracer:
    # Access any module's output
    hidden_states = model.transformer.h[5].output[0].save()

    # Access attention patterns
    attn = model.transformer.h[5].attn.attn_dropout.input[0][0].save()

    # Modify activations
    model.transformer.h[8].output[0][:] = 0  # Zero out layer 8

    # Get final output
    logits = model.output.save()

# After context exits, access saved values
print(hidden_states.shape)  # [batch, seq, hidden]

Proxy Objects

Inside `trace`, module accesses return Proxy objects that record operations:

with model.trace("Hello"):
    # These are all Proxy objects - operations are deferred
    h5_out = model.transformer.h[5].output[0]  # Proxy
    h5_mean = h5_out.mean(dim=-1)              # Proxy
    h5_saved = h5_mean.save()                   # Save for later access

Workflow 1: Activation Analysis

Step-by-Step

from nnsight import LanguageModel
import torch

model = LanguageModel("gpt2", device_map="auto")

prompt = "The capital of France is"

with model.trace(prompt) as tracer:
    # 1. Collect activations from multiple layers
    layer_outputs = []
    for i in range(12):  # GPT-2 has 12 layers
        layer_out = model.transformer.h[i].output[0].save()
        layer_outputs.append(layer_out)

    # 2. Get attention patterns
    attn_patterns = []
    for i in range(12):
        # Access attention weights (after softmax)
        attn = model.transformer.h[i].attn.attn_dropout.input[0][0].save()
        attn_patterns.append(attn)

    # 3. Get final logits
    logits = model.output.save()

# 4. Analyze outside context
for i, layer_out in enumerate(layer_outputs):
    print(f"Layer {i} output shape: {layer_out.shape}")
    print(f"Layer {i} norm: {layer_out.norm().item():.3f}")

# 5. Find top predictions
probs = torch.softmax(logits[0, -1], dim=-1)
top_tokens = probs.topk(5)
for token, prob in zip(top_tokens.indices, top_tokens.values):
    print(f"{model.tokenizer.decode(token)}: {prob.item():.3f}")

Checklist

• [ ] Load model with LanguageModel wrapper
• [ ] Use trace context for operations
• [ ] Call `.save()` on values you need after context
• [ ] Access saved values outside context
• [ ] Use `.shape`, `.norm()`, etc. for analysis

Workflow 2: Activation Patching

Step-by-Step

from nnsight import LanguageModel
import torch

model = LanguageModel("gpt2", device_map="auto")

clean_prompt = "The Eiffel Tower is in"
corrupted_prompt = "The Colosseum is in"

# 1. Get clean activations
with model.trace(clean_prompt) as tracer:
    clean_hidden = model.transformer.h[8].output[0].save()

# 2. Patch clean into corrupted run
with model.trace(corrupted_prompt) as tracer:
    # Replace layer 8 output with clean activations
    model.transformer.h[8].output[0][:] = clean_hidden

    patched_logits = model.output.save()

# 3. Compare predictions
paris_token = model.tokenizer.encode(" Paris")[0]
rome_token = model.tokenizer.encode(" Rome")[0]

patched_probs = torch.softmax(patched_logits[0, -1], dim=-1)
print(f"Paris prob: {patched_probs[paris_token].ite