This release is a pre-release and may not be stable for production use.
Interpret and manipulate the internals of deep learning models
Documentation | GitHub | Discord | Forum | Twitter | Paper
About
nnsight lets you get inside a model's forward pass. Open a with model.trace(...)
block and write ordinary Python against any internal value — a layer's output, an
attention pattern, a gradient — as if you already had it: read it, edit it, save it.
You don't register hooks or refactor the model; you write the intervention in the
order it happens, and nnsight runs it interleaved with the real forward pass.
The same trace runs on a model on your laptop or, with remote=True, on a model far
too large for it via the NDIF infrastructure. nnsight works with
any PyTorch model and ships wrappers for HuggingFace, diffusers, and vLLM.
📖 For how it works under the hood — tracing, interleaving, the envoy tree — read NNsight.md. Task recipes live under
docs/.
Installation
pip install nnsight
Quick start
from nnsight import TransformersModel
model = TransformersModel("openai-community/gpt2", dispatch=True)
with model.trace("The Eiffel Tower is in the city of"):
# edit a layer's output in place — the model computes on the edited value
model.transformer.h[0].output[:] = 0
# read a hidden state further down (a [batch, seq, hidden] tensor)
hidden = model.transformer.h[6].output.save()
# keep the final logits
logits = model.output.logits.save()
print(hidden.shape) # torch.Size([1, 10, 768])
print(logits.argmax(-1)) # next-token predictions
Inside the block you're not running the model — you're describing what to do when it
runs. Reading .output gives you the real tensor once the model reaches that module;
assigning to it splices your value in. Mark anything you want after the block with
.save() (or nnsight.save(x)).
A GPT-2 block's
.outputis a plain tensor; some modules (like attention) return a tuple, so you'd index.output[0].print(model)orprint(module.source)shows the shape.
What you can do
Generate — generate returns token ids on tracer.result:
with model.generate("The Eiffel Tower is in", max_new_tokens=3) as tracer:
ids = tracer.result.save()
print(model.tokenizer.decode(ids[0])) # "The Eiffel Tower is in the middle of"
Reach into each generation step — save a container, append raw values (use a bounded range so code after the loop still runs):
import nnsight
with model.generate("Hello", max_new_tokens=5) as tracer:
tokens = nnsight.save([])
for step in tracer.iter[:5]:
tokens.append(model.output.logits[0, -1].argmax(-1))
Batch several prompts in one pass — each invoke block sees only its own rows:
with model.trace() as tracer:
with tracer.invoke("The Eiffel Tower is in"):
eiffel = model.transformer.h[-1].output[:, -1].save()
with tracer.invoke("The Great Wall is in"):
wall = model.transformer.h[-1].output[:, -1].save()
Take gradients with respect to an internal value:
with model.trace("The Eiffel Tower is in the city of"):
hidden = model.transformer.h[-1].output
with model.output.logits.sum().backward():
grad = hidden.grad.save()
Apply modules out of order (a logit lens — decode a middle layer through the head):
with model.trace("The Eiffel Tower is in the city of"):
hidden = model.transformer.h[-1].output
token = model.lm_head(model.transformer.ln_f(hidden))[0, -1].argmax(-1).save()
print(model.tokenizer.decode(token)) # " Paris"
Run it remotely on NDIF — the same trace, on a model you can't host:
from nnsight import CONFIG
CONFIG.set_default_api_key("YOUR_NDIF_KEY")
model = TransformersModel("meta-llama/Llama-3.1-8B")
with model.trace("The Eiffel Tower is in", remote=True):
hidden = model.model.layers[-1].output.save()
There's more — source tracing into a module's forward (.source), persistent
edit(), skip(), scan() for shapes, cache(), session(),
and the vLLM and diffusion runtimes. See docs/ and
NNsight.md.
Your own model
Any torch.nn.Module works — wrap it in NNsight and the whole tree becomes
traceable:
import torch
from nnsight import NNsight
net = torch.nn.Sequential(torch.nn.Linear(5, 10), torch.nn.Linear(10, 2))
model = NNsight(net)
with model.trace(torch.rand(1, 5)):
model[0].output[:] = 0 # zero the first layer's output
out = model.output.save()
print(out) # [1, 2], computed with layer 0 zeroed
Using nnsight from an LLM agent
Give an agent up-to-date nnsight knowledge one of these ways:
- Skills — in Claude Code:
/plugin marketplace add https://github.com/ndif-team/skills.gitthen/plugin install nnsight@skills. In OpenAI Codex:skill-installer install https://github.com/ndif-team/skills.git. - Context7 MCP — add
use context7to prompts, or point your MCP client athttps://mcp.context7.com/mcp(see Context7). - Docs in context — hand the agent CLAUDE.md (routes to the task docs) and NNsight.md (the internals).
Learn more
- nnsight.net — tutorials, guides, API reference
- NNsight.md — the design-and-implementation manual
- CLAUDE.md + docs/ — the task reference
- nnsight.net/status — models available on NDIF
Citation
If you use nnsight in your research, please cite:
@article{fiottokaufman2024nnsightndifdemocratizingaccess,
title={NNsight and NDIF: Democratizing Access to Foundation Model Internals},
author={Jaden Fiotto-Kaufman and Alexander R Loftus and Eric Todd and Jannik Brinkmann and Caden Juang and Koyena Pal and Can Rager and Aaron Mueller and Samuel Marks and Arnab Sen Sharma and Francesca Lucchetti and Michael Ripa and Adam Belfki and Nikhil Prakash and Sumeet Multani and Carla Brodley and Arjun Guha and Jonathan Bell and Byron Wallace and David Bau},
year={2024},
eprint={2407.14561},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.14561},
}
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