Chrome DevTools for AI models and agents.
See inside any local LLM, VLM or robot policy while it runs.
Open a .mri·
Docs
Build log
pip install modelmri && modelmri serve # → http://localhost:5900
Hover any token — arcs show what it attended to. Every layer, every head. Light and dark.
| 🔍 | You cannot debug what you cannot see. Your model gave a wrong answer. The logs show the prompt and the output, and nothing in between. | | 🖥️ | It runs on your machine. No cloud, no account, no telemetry, no API key. An 8 GB laptop GPU is the target, not a footnote. | | 🧾 | Every number carries a receipt. What was measured, on which model revision, with how many forward passes, and what it does not prove. | | 🚫 | It refuses rather than guesses. When a measurement would be misleading, you get a sentence explaining why — not a plausible number. |
| 👁️ | Attention |
Every layer, every head, from a live forward pass — with a causal ranking of which heads actually mattered, scored in KL nats. |
| 🎯 | Activation patching |
Where in the model the answer is decided, on a (layer × position) grid — each site checked against eight same-norm random draws. |
| 🧠 | Concepts |
SAE features, or contrastive steering vectors when no SAE exists. Find one, turn it off, watch the output change. |
| 🔭 | Lenses |
Logit lens and a tuned lens trained on your text, scored on held-out KL and shown side by side. |
| 🤖 | Robot policies |
What a VLA looked at, and — through a sidecar with its own environment — what it would do. |
| 🕵️ | Agent traces |
The step where your agent died, as a timeline you can click into the model's internals from. |
| 🧩 | Your own models |
Any nn.Module , TorchScript, or GGUF. Nothing hardcoded to one architecture. |
| 🔒 | Weight scanning |
Looks inside a checkpoint for anything that executes on load, before it. |
Activation patching: which (layer, position) actually decides the answer.
pip install modelmri
modelmri serve # the UI, on localhost only
modelmri models # what is already on this disk
modelmri scan ./my_model # is anything in there executable?
modelmri open finding.mri # someone sent you a result
Python 3.10+ · Windows, macOS, Linux · MIT.
It finds the models you already have — HF cache, plain folders, GGUF — before asking you to type anything.
Type a prompt, watch it stream, then hover any token — arcs show which earlier tokens it looked at, scaled by attention weight, for any layer and head.
The generated token
" Paris"
attends back to" capital"
and" France"
. The information was always there. Nobody was looking.
144 heat maps and no reason to open any of them is a browsing tool. Rank heads zeroes each head in a layer, runs the model again, and measures how far the answer moves — so the dropdown arrives ordered and the top head is already selected.
Qwen3-1.7B · "The capital of France is" · answer " Paris" · zero-ablation · bf16
Rank heads → L0 H3 KL 1.954 p(" Paris") 0.539 → 0.029 changes the answer
L0 H9 KL 0.096 p(" Paris") 0.539 → 0.345
L0 H1 KL 0.054 p(" Paris") 0.539 → 0.475
L0 H5 KL 0.044
L0 H11 KL 0.035
18 forward passes · 5.6 s · noise floor 0.0
One head in the first layer carries most of it: removing L0 H3 alone takes
" Paris"
from 0.539 to 0.029 and the model answers something else. The next head down moves it by a twentieth as much.
The setup line is not decoration. A KL depends on the model, the prompt, the dtype and the sequence — the same heads on one model give different numbers in fp32 and over a long generation — so a figure quoted without them cannot be checked by anyone.
Three baselines, and how much they disagree is itself a property of the
model. Zeroing a head is one choice; replacing it with its own mean is
another; replacing it with what it really computes on a different sentence
(resample
, eight draws) is the only one that keeps the model on its own
distribution. On Qwen3-1.7B
layer 0 they broadly agree — Spearman 0.81 to 0.91, and the top five differ by at most one head. On other architectures they disagree far more. The panel reports that number so the choice of baseline is visible rather than silently deciding the ranking.
Resampling also shows its own spread, because one donor is a coin flip. Head 3
on Qwen3-1.7B
scored between 3.016 and 5.904 across the eight draws around a median of 4.540 — a spread wide enough that a single draw could have reported almost any number in it as the head's importance.
A ranking costs n_heads + 2
forward passes; the whole model costs n_layers × n_heads + 2
. That is the part that is portable — Qwen3-1.7B is 450 (28 layers x 16 heads). What a pass costs on your machine is not: measured on one RTX 4060 across sessions it moved between 12 and 71 ms for the same model, so the panel measures a layer on your machine and extrapolates from that rather than quoting a number from mine. One layer by default; the whole model only when told, with the estimate shown first.
Then ask what changes? on any ranked head and the panel subtracts the two runs — arcs in one colour where the model attends more without that head, another where it attends less. It opens at layer L+1, because removing a head cannot change its own layer's attention (that layer's input is unchanged), and a zero result says so rather than showing you an empty canvas.
Both sides are forward passes over the same token sequence, never two generations — sampling diverges, and chat templates insert 0, 8 or 29 leading tokens depending on the model, so subtracting two generations would align token 5 of one against token 5 of a different sentence.
It reports what it measured and nothing more. These are not each head's share of the prediction — the per-head scores do not sum to the whole layer's ablation, in either direction — and the ranking depends on what a removed head is replaced with, so the baseline is named on screen and both are offered. head_dim
is read from the model rather than computed as hidden_size // n_heads
, which is wrong by 2× on Qwen3 and would rank half-heads confidently.
Ablation says what mattered. It cannot say where the thing is. Patching takes two prompts that differ in one fact, moves an activation from the run that knows the answer into the run that does not, at every (layer, position), and reports how much of the difference comes back.
Blue recovered the clean answer, red pushed it further away. Ringed cells were tested against chance.
clean "The Eiffel Tower is located in the city of" -> " Paris"
corrupt "The Colosseum is located in the city of" -> " Rome"
Three grids, because where and through what are different questions — and they disagree. Measured on the same pair across three architectures:
| model | residual | attention | MLP |
|---|---|---|---|
Qwen2.5-0.5B-Instruct |
|||
+0.999 · L23 · of |
|||
+0.478 · L21 · of |
|||
+0.721 · L0 · os |
|||
gemma-3-270m-it |
|||
+1.010 · L17 · of |
|||
+0.736 · L12 · of |
|||
+0.483 · L3 · osseum |
|||
Qwen3-1.7B |
|||
+0.967 · L3 · el |
|||
+0.651 · L20 · of |
|||
+0.444 · L22 · of |
Across the first three, the MLP peak sat on a subject token in an early
layer — um
, os
and osseum
are all pieces of "Colosseum" — while the
attention peak sat on the last token, late. That was a tidy story, and
Qwen3-1.7B
breaks it: its MLP peak is at layer 22 on the final token, and its residual peak moved the other way, to layer 3 on a subject token.
Three models is not a result. The generalisation is left here with the model that falsifies it rather than quietly rewritten, because the shape of this table is the actual finding: where a fact lives is a property of the architecture, not of transformers. Run it on yours — the panel does this on whatever you have loaded, and the answer is not knowable from the first three.
The score is signed, and it is the one ranking here that is not a KL: a patch can push the answer further away, and 5 of 132 sites did. It is also not capped at 1.0 — a single site can overshoot, and gemma-3-270m-it
reads 1.010.
Each of the strongest sites is run again against eight same-norm random draws at the same site, not one, because one is a coin flip: at a single site the draws ran from −2.038 to +0.616 against a real recovery of +0.427.
Most casually-written pairs are refused, and both failures are invisible unless you are told — the prompts must tokenize to the same length (2 of 8 natural minimal pairs did not) and must predict different tokens (2 of 3 did not, making the denominator exactly 0). Both refusals print what to change.
Load a sparse autoencoder and ModelMRI shows the human-interpretable features firing on every token. Click one, drag the slider, and run a deterministic A/B:
gpt2 · layer 8 · jbloom/GPT2-Small-SAEs-Reformatted · FVU 0.0010 · 60.5 features/token
prompt The Eiffel Tower is located in the city of
baseline Paris, France.
feature #5856 @ -40 London's central London borough.
#5856 is the top-firing feature on the final prompt token — activation 35.55, the one the panel already has selected — so this example is reachable by following the instructions above rather than by knowing which number to type. Same prompt, greedy decoding, no prompt tricks. We reached into layer 8 and turned the concept down. Clearing the steer restores the baseline byte for byte.
The SAE checks itself before it shows you anything. An SAE fed the wrong
activation convention does not error — it returns features, in the right shape,
with plausible magnitudes, for a vector it never saw. So ModelMRI measures which
convention actually reconstructs (centered along d_model
or not, b_dec
subtracted from the input or not), reports the fraction of variance unexplained, and refuses to plot anything when no convention reconstructs. On the default SAE that is the difference between 60.5 features firing per token and 7,491, and between an FVU of 0.0010 and 13,579.
Two lines of modelmri.record
around any agent run gives you a timeline: LLM calls, tool calls, subagents, each as a block. The failure glows. Click it for the exact input, output, tokens, and error.
from modelmri.record import trace, step
with trace("fix-failing-tests"):
step("llm_call", name="plan", input=prompt, output=answer, tokens_in=912)
with step("subagent", name="auth-fixer"):
step("tool_call", name="pytest", output="17 passed")
Or instrument automatically: modelmri.record.instrument_anthropic()
.
This is the part nobody else ships. ModelMRI loads the vision tower of the real SmolVLA checkpoint and runs actual robot-camera frames through it, painting each image patch's attention back onto the frame. Scrub an episode, run the policy, drag the layer slider.
Measured on PushT frames — share of attention mass in the top 5% of patches:
| vision layer | concentration |
|---|---|
| 0 | 27% |
| 6 | 56% |
| 11 | 60% |
Early layers look everywhere; deep layers lock on. No robot hardware required — it reads public LeRobot datasets straight from disk.
Everything above is transformer-shaped. This isn't. Point ModelMRI at your own nn.Module
— an MLP, a small CNN, whatever you're training — and get a layer-by-layer map of one real forward pass.
def load():
model = MyNet()
model.load_state_dict(torch.load("checkpoints/best.pt", map_location="cpu"))
return model
| layer | type | output | activation | |
|---|---|---|---|---|
fc1 |
||||
| Linear | 8×64 | −31.20 ± 24.21 | ||
act1 |
||||
| ReLU | 8×64 | 0.10 ± 0.26 | 80% dead | |
fc2 |
||||
| Linear | 8×32 | −1.02 ± 4.74 | ||
act2 |
||||
| Tanh | 8×32 | −0.12 ± 0.90 | 55% saturated | |
head |
||||
| Linear | 8×3 | −0.13 ± 0.44 |
Dead units, saturated activations, and the first layer where a nan appears — statistics exclude non-finite values on purpose, so one bad number can't turn every row below it into
nan
and hide where it started.A state_dict
alone is refused, with the reason: it's weights without an architecture, and guessing one would produce a map that looks authoritative and describes a network you never trained.
You found the head. Now show a colleague — who does not have your GPU, your prompt, or 8 GB of spare disk.
That writes one 54 KB file holding the tokens, the attention, the generation and your note. No weights — it's an observation, not a checkpoint.
The recipient opens it at the viewer — nothing installed, nothing uploaded, the file is read in their browser.
Locally it's the same page, served from the package by the standard library:
modelmri open 0000.mri # ~0.3s — no torch, no model, no GPU
If you were sent several and want to know which is which, inspect
prints one without opening anything:
modelmri inspect 0000.mri
0000.mri — 83.8 KB
model Qwen/Qwen3-1.7B
size 1,721M parameters
ran on cuda:0 · bfloat16
recorded 2026-08-18T08:38:51+00:00 by ModelMRI 0.11.0
note sweep row 0: The capital of France is
tokens 21 (13 prompt)
shape 28 layers x 16 heads
attention 448 maps
every layer and head
patching attn, mlp, resid
clean The Eiffel Tower is located in the city of
corrupt The Colosseum is located in the city of
prompt The Eiffel Tower is located in the city of
answer Paris
--json
gives the same summary as a document, with the prompt and generation untruncated. Both take the same ~0.2s as open
: a .mri
is gzipped JSON, so nothing here needs torch.
A recording carries the activation-patching trace too, when one was run — so the causal finding travels with the file, not just the attention. Open a .mri
that has one and the patching panel draws it, marked as recorded rather than measured on your machine. A recording that carries none says so instead of offering a button that can only refuse: patching means running the model again with an activation replaced, and a .mri
holds activations rather than weights.
Every panel reads a recording through the same calls it uses for a live model, so the arcs, the layer/head dials and the token strip all behave normally. The status pill says replay
and the footer says recorded, not live, so it can never be mistaken for your own run.
The browser viewer and the Python tool are checked cell-for-cell against the same file on every change (tests/viewer_check.py) — a viewer that renders a slightly different matrix would be worse than no viewer, because nothing on screen would say so.
Nine of them, and only one needs a GPU. Everything below reads what is already on your disk — no model is downloaded and no server is started unless the command says so.
| Command | What it does | Needs |
|---|---|---|
pip install modelmri |
||
Install it. modelmri[vla] adds the robot-policy reader; modelmri[dev] adds the test suite. |
||
| — | ||
modelmri serve |
||
Start the app and open it at localhost:5900 . This is the one that loads models. --port , --host . |
||
| a model | ||
modelmri models |
||
| List every model on this machine, and the ones that will not load, with the reason. Instant. | ||
| — | ||
modelmri traces |
||
| List agent runs recorded here, newest first. Instant. | — | |
modelmri open FILE.mri |
||
| Open an analysis somebody sent you, in a browser. No model, no GPU, ~0.3s. | — | |
modelmri inspect FILE.mri |
||
Print what a .mri holds and exit — model, shape, what was captured, the prompt. --json for the lot. ~0.2s. |
||
| — | ||
modelmri diff A.mri B.mri |
||
Compare two analyses of the same prompt and exit non-zero when something moved. --fail-over X for CI. No torch — instant. |
||
| — | ||
modelmri sweep PROMPTS |
||
Run one measurement over many prompts and report each head as median, IQR, n and top-k rate instead of one number. --metric , --layer , --jsonl , --out-dir . |
||
| a model | ||
modelmri verify FILE.mri |
||
Re-run the measurements in a and report, per number, whether it came back the same. .mri on this machine--json for CI. Loads the model. |
||
| the file's model | ||
modelmri doctor |
||
| What this machine can and cannot run, and why. Run it before you file a bug. | — | |
modelmri where |
||
| Every directory ModelMRI reads or writes, and the variables that move them. | — | |
modelmri uninstall |
||
Remove everything ModelMRI has written here. --models takes the weights too. Asks first. |
||
| — |
The two you will use most
modelmri models # what have I got, and why won't that one open?
modelmri serve # look inside one of them
Sending someone a finding
modelmri inspect 0000.mri # what is in this file?
modelmri open 0000.mri # show me
modelmri verify 0000.mri # do these numbers come back on my machine?
Checking a finding instead of trusting it
modelmri verify 0000.mri
0000.mri — measured on Qwen/Qwen3-1.7B
file: bfloat16 on cuda:0 here: bfloat16 on cuda:0
commit: 607a30d783df (same weights)
✓ generation: reproduced
greedy decoding produced the same 4 tokens.
✓ attention: reproduced
all 144 stored head maps match. The worst, 6:9, is off by 2.00e-03
against a 3.92e-03 tolerance.
✓ patching: reproduced
all 3 grids match to 0.00e+00, inside a 0.00e+00 floor measured by
running the same trace twice here.
– head ranking: not verifiable
this file records that a head ranking ran, but the `.mri` carries
attention, patching and the generation — not the ranking itself.
3 reproduced · 0 differ · 1 not verifiable
Three verdicts and no pass/fail, because bit-exact reproduction across two machines is not achievable — kernel selection, cuDNN version and TF32 all move the last digits. Every tolerance above was measured on the machine running the command, never asserted from a constant: each check runs the same computation twice locally and takes the spread, and for attention the file supplies a second floor of its own, since it stores each map as uint8 against that map's maximum. Exit 1 only for a real disagreement — a file this machine cannot check is not a broken file.
No hosted platform can offer this. It can hand you its own assertion; it can never hand you the re-run.
One prompt is an anecdote
modelmri sweep prompts.txt --model Qwen/Qwen3-1.7B --layer 0
heads over 5 prompts on Qwen/Qwen3-1.7B · baseline zero
5 measured · 0 could not be measured
head median IQR range n top5
L0H3 0.00492 0.00578 0.00198–0.01077 5 5/5 (100%)
L0H12 0.00006 0.00004 0.00005–0.00013 5 5/5 (100%)
L0H7 0.00004 0.00001 0.00004–0.00006 5 5/5 (100%)
L0H2 0.00001 0.00000 0.00001–0.00002 5 5/5 (100%)
L0H1 0.00001 0.00000 0.00001–0.00002 5 4/5 (80%)
L0H0 0.05418 0.04382 0.03673–1.71640 5 3/5 (60%)
Read the fifth row. L0H0 has a median of 0.054 and a maximum of 1.716 — it carried one prompt almost entirely and did nothing on the rest. That is the head worth looking at, and it is the head a mean would have buried. This is what "a number measured once is a sample, not a property" looks like as behaviour rather than as a line in a readme.
Three rules it enforces rather than documents: never a mean without a spread; a prompt that could not be measured is a row carrying the reason, never a gap; and a token-position sweep is never aggregated across prompts, because position 3 is a different word in every one of them.
Did my quantisation change the model?
modelmri diff baseline.mri after-quantising.mri --fail-over 0.05
baseline.mri → after-quantising.mri
= generation: same
≠ head ranking: changed
the top 5 changed: L0H4 entered and L0H7 left. 12 of 12 heads moved
past the 0.00e+00 noise floor.
= attention: same
≠ patching: changed
3 patching sites changed sign — a site that recovered the clean answer
and now pushes away from it is a different causal story.
Exit 1, in the pull request that did it. Nothing else in the category has a regression concept for model internals; the state of the art for this question is a Reddit thread. It imports no torch — both sides are already measured and comparing them is arithmetic — so it is a CI step you would actually add. The guide has a workflow you can paste.
It refuses rather than guesses: two different prompts exit 2 instead of being diffed into numbers that look like a regression, a sampled run is refused because it differs for reasons that are not the model, and a section one file lacks reports "not comparable", never "same".
A .mri
is one analysis without the model — attention, the logit lens, the generation, and the activation-patching trace if you ran one. It is how you show a colleague the head you found without asking them to download 8 GB.
When something is wrong
modelmri doctor # can this machine run it at all?
modelmri where # where did it put my stuff?
| Variable | Moves |
|---|---|
MODELMRI_MODELS_HOME |
|
| where downloaded models land (default: wherever HuggingFace already puts them) | |
MODELMRI_MODELS_DIR |
|
| extra folders to search for your own models | |
MODELMRI_HOME |
|
| all of ModelMRI's own state, under one directory | |
MODELMRI_TRACE_DIR |
|
| where undelivered agent traces are written | |
MODELMRI_DEVICE |
|
force cpu , cuda , mps , xpu |
|
HF_HOME / HF_HUB_CACHE |
|
| HuggingFace's own cache, honoured as-is |
pip install modelmri # core: playground, attention, features, steering, agents
pip install "modelmri[vla-lite]" # + robot datasets (av, pyarrow, pillow)
pip install modelmri-record # just the agent recorder — stdlib only, an 10.9 KiB wheel
modelmri doctor # what this machine can and cannot run, measured
modelmri serve
Will it run here? modelmri doctor
measures your machine and says so — OS,
cores, RAM, free disk, the torch build, the accelerator it found and its
precision, and roughly how large a model fits. It exits non-zero when something
would stop a load, so it is scriptable. modelmri serve
prints the one-line version at startup.
accelerator NVIDIA GeForce RTX 4060 Laptop GPU (cuda) vram 8.6 GB bfloat16
Models up to roughly 3.2B parameters should fit.
Every figure is read off the machine at the moment you ask, and a number that
cannot be determined says "could not measure" rather than being invented. Note
this is a first-run check rather than an install-time one, deliberately: a
wheel is an archive and pip does not execute code from it, so there is nowhere
honest to put a check during pip install
. It is also the better place for it,
because the same machine can be perfectly able to open a shared .mri
and unable to load a 7B model — and those are different questions.
From source:
git clone https://github.com/muhammadmahadazher/ModelMRI && cd ModelMRI
cd frontend && npm ci && npm run build && cd ..
uv sync && uv run modelmri serve
Models. Search HuggingFace, pick from what's already cached on your machine, or switch to Ollama and pull any model by name. (Ollama gives you text only — internals need a HuggingFace model, and ModelMRI says so rather than pretending.)
Nothing downloads by surprise. Every row shows its size before you click, and a download that cannot fit your disk is refused with both numbers rather than started. One that dwarfs your GPU asks first. Whatever is running, Stop actually stops it — the fetch happens in a child process precisely so it can be killed, and the half-written blobs are cleaned up. This exists because a click once began fetching 1.5 TB onto an 8 GB laptop with no way out but killing the server.
GPU when you have one. NVIDIA, AMD, Intel and Apple silicon are detected automatically and the badge explains its choice — including the common case where torch was installed as a CPU-only build, where it prints the exact command to fix it. CPU works fine too; a 0.5B model streams in a couple of seconds.
The UI is a client of a plain HTTP API — script against it directly.
POST /api/model/load |
{hf_id, source} — "hf" or "ollama" |
WS /ws/generate |
stream tokens |
GET /api/attention |
?layer=&head= → tokens + attention matrix |
POST /api/sae/load · GET /api/features/summary |
SAE features per token |
POST /api/steer |
{feature_id, scale} — clamp a concept during generation |
POST /api/traces/import · GET /api/traces/{id} |
agent traces |
POST /api/vla/analyse · GET /api/vla/attention |
robot-policy attention |
POST /api/custom/load · POST /api/custom/run |
inspect a model you trained yourself |
| Playground · streaming · any local model · Ollama | ✅ |
| Attention inspector | ✅ |
| Head ranking by ablation | ✅ |
| Compare two runs (signed attention diff) | ✅ |
| SAE feature browser + activation steering | ✅ |
| Agent trace timeline + step inspector | ✅ |
| Robot policy (VLA) attention over real episodes | ✅ perception |
| Custom models — adapters, TorchScript, layer map | ✅ |
Shareable .mri sessions + zero-install browser viewer |
✅ |
| Download size guard + a Stop button that works | ✅ |
VLA action expert (needs lerobot , separate env) |
🏗️ |
| Hosted zero-install demo | ✅ |
Attention needs eager attention. SDPA and FlashAttention never materialize the weights, so ModelMRI loads models withattn_implementation="eager"
. Slower, but it's the only way to see anything.SAE features need an SAE that exists. They are trained per model, and public ones exist for only a handful — this build knows of four repositories — so there is none for most of what you will load, and no amount of code makes one appear. ModelMRI offers the one that matches your model, says plainly when there is none, and falls back to a logit lens, which needs nothing but the model.Custom models get a layer map, not attention. Attention and SAE features need a transformer; for an arbitrarynn.Module
ModelMRI shows shapes, activation statistics and pathologies. an adapter runs your Python — seeSECURITY.md.VLA mode is the perception half. SmolVLA's vision tower is real and loaded from the real checkpoint; the action expert needslerobot
, whose torch/numpy pins conflict with the core runtime, so it lives behind an opt-in extra rather than degrading everyone's install.
The parts of ModelMRI are not new. Attention visualization, causal ablation, sparse autoencoders and agent tracing all have good tools already, and most of them do their one thing better than ModelMRI does. What is unusual here is the combination — model internals and agent traces, in one GUI, on your hardware, with no notebook in between.
| what it is | where ModelMRI differs | |
|---|---|---|
TransformerLensnnsightNeuronpediayourprompt, locally, and steers with itSAELensLangfuse·Phoenix·LangSmithpromptfoo ModelAuditmodelmri scan
runs on the load path and refuses to load oneRerun·Foxgloveattended to, or what it would do on a frame you chooseThe one thing nothing else does: hold the recorder and the weights in one process. Every observability platform stops at the API boundary; every interpretability library has no agent traces. Joining a failing agent step to the heads that moved the token needs both in memory at once.
Use TransformerLens if you are doing research and want precision. Use Langfuse or Phoenix if you are running an agent in production and need dashboards, retention and alerting. Reach for ModelMRI when you have a model on your machine that is doing something you do not understand, and you want to look at it now.
An open-source, local-first tool for inspecting what a model is doing
internally while it runs: attention per layer and head, which heads carry the
prediction, which interpretable features fire, what changes when you turn one
off — plus a recorder that makes an agent run inspectable step by step.
pip install modelmri && modelmri serve
, then open http://localhost:5900
.
Not for internals, and no tool can — closed API models do not expose attention weights or activations to anyone outside the provider. ModelMRI needs weights it can run, so internals mean a local HuggingFace model.
The agent recorder is a different matter: it wraps whatever your agent
calls, including hosted APIs, so you can record and inspect a run driven
entirely by Claude or GPT-4. modelmri.record.instrument_anthropic()
does it in one line.
No. CPU works — a 0.5B model streams in a couple of seconds. NVIDIA, AMD, Intel and Apple silicon are detected automatically when present, and if torch was installed as a CPU-only build the badge says so and prints the command to fix it.
Any causal LM transformers can load with eager attention, from the HuggingFace
cache you already have, a plain folder on disk, or a search-and-download in
the app. The ones with a recorded end-to-end result are GPT-2, Qwen3-0.6B,
Qwen2.5-0.5B-Instruct, SmolLM2-360M-Instruct and Gemma-3-270m-it — the
verified table
lists what each one actually measured. Others should work and are not claimed
to have been checked: Llama-3.2-1B is deliberately absent because the
meta-llama
repos are gated and returned 403 for the account used, and an untested model in a list headed "supported" is just a guess in bold. Ollama models work for text generation; internals need a HuggingFace model, and ModelMRI tells you that instead of quietly showing you nothing. Non-transformer models you trained yourself get a layer map with activation statistics.
No. There is no cloud, no account and no telemetry. Models are downloaded from
HuggingFace if you ask for one you don't have; beyond that, nothing leaves the
machine. A .mri
file you choose to share contains tokens, attention and your note — never weights — and the browser viewer reads it client-side without up it.
One ~54 KB file holding the tokens, the attention matrix, the generation and a
note you wrote. It exists so you can send a colleague the finding without
sending them 8 GB of weights or asking them to install anything — they open it
at the viewer in a
browser. modelmri open file.mri
does the same locally in about 0.3s, with no torch and no GPU.
BertViz draws attention; ModelMRI also measures which heads matter by removing them. TransformerLens is a library you write code against and is more precise and more flexible than this; ModelMRI is a UI for the questions people ask most, and adds SAE steering, agent traces and robot policies in the same window. See How it compares.
No, and the package says so — it is classified alpha. It is a debugging and research tool, not infrastructure. The measurements it reports are tested, but the API surface still moves between minor versions; see the changelog.
Yes. modelmri scan <path>
reads the pickle opcode stream without
unpickling it and reports anything that executes on load — os.system
,
eval
, decode-then-execute chains, embedded executables, zip bombs. It exits non-zero on a finding, so it works as a CI gate.
It runs automatically on the load path, so a dangerous file is refused rather
than reported after the fact. Three verdicts: safe
, dangerous
, and
unscanned
— a format it could not read is never called clean.
A .bin
, .pt
, .pth
or .ckpt
is a pickle, and unpickling is not parsing:
the payload runs before a single tensor is read. safetensors
has no mechanism to execute anything, and ModelMRI tells you when a repository publishes one.
Yes, through a sidecar that holds the action expert in its own process and virtual environment — because lerobot pins torch and numpy hard enough that installing it beside ModelMRI breaks ModelMRI.
modelmri policy install
builds it; modelmri policy start
runs it. Then you get predicted-versus-recorded actions across an episode, an instruction-swap test measured against the policy's own sampling variance, and input-stream knockout.
It refuses to overlay a policy's actions on a dataset's recorded ones when the two are in different units — which is the common case, and the plausible-wrong chart everything else draws.
Because a measurement that would be misleading is worse than no measurement. Some real examples it will refuse:
- an SAE whose activation convention does not reconstruct your model
- a patching pair whose two prompts predict the same token, making the denominator zero
- an instruction-swap test on a deterministic policy, where the reference spread is exactly zero
- two action curves whose units were never published
Every refusal is a sentence naming what to change.
CITATION.cff is in the repository root — GitHub's "Cite this repository" button reads it. Please include the version, because the measured figures in this README belong to the release that produced them.
Issues and pull requests are welcome. One rule runs the whole repository: don't ship a measurement you haven't verified. A visualization that looks plausible and is wrong is worse than none, because interpretability is exactly the domain where nobody has an independent way to notice.
Contributing guide— setup, quality gates, and the three bugs that made that ruleCode of conductSecurity policy— trust model, credential handling, and what a model actually executesSupport·Changelog
Notes, mistakes, and what broke: modelmri.substack.com
MIT © Muhammad Mahad Azher