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 (730+ stars) Paper: NNsight and NDIF: Democratizing Access to Foundation Model Internals (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 for an API key.

Core Concepts

LanguageModel Wrapper

from nnsight import LanguageModel