ModelMRI

See inside the model. Attention, concepts and steering for any local model — plus a flight recorder for your agents. One pip install, everything on your machine.

pip install modelmri
modelmri serve

Then open http://127.0.0.1:5900.

Just want to trace an agent?

You don't need the viewer's dependencies. pip install modelmri-record is stdlib only — 7 KiB, no torch. See Recording agents.

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What it does

• Attention — where each token looked

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  Generate, then hover any token to see the arcs it attended to. Every layer, every head, computed on demand from a real forward pass with output_attentions=True.

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• Features — the concepts inside

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  A sparse autoencoder over the residual stream: 24,576 interpretable features. Click a token to see what fired, click a feature to see where it fires across the sequence.

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• Steering — change the answer

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  Add a feature's direction to the residual stream and regenerate. Same prompt, same seed, different output — an A/B you can actually run.

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• Agents — a flight recorder

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  Record LLM calls, tool calls and subagents from your own code, then read the run as a timeline instead of scrolling logs.

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• Your own models

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  Point it at a network you trained yourself and get a layer map of one real forward pass — shapes, activation ranges, dead units, and the first layer where a nan appears.

  Read more →

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Why local-first

Everything runs on your machine. No account, no upload, no telemetry. The model weights, the prompts, the traces and the credentials all stay where they are — which is the only arrangement under which you can point this at real work.

The trade is honest: you need the hardware for whatever model you load. A 0.5B model is comfortable on a laptop GPU; a 7B one is not.

Verified, not asserted

Every model in the table below was run end to end on an RTX 4060 laptop in bfloat16, with attention rows summing to 1.000 and the causal mask intact.

model	params	shape
Qwen3-0.6B	596M	28 layers × 16 heads
Qwen2.5-0.5B-Instruct	494M	24 × 14
SmolLM2-360M-Instruct	362M	32 × 15
Llama-3.2-1B-Instruct	1236M	16 × 32
Gemma-3-270m-it	268M	18 × 4
GPT-2	124M	12 × 12

Any causal LM on the Hub should work; those are the ones actually tested.

Licence

MIT.