metrik-explorer
Architecture, parameter, and memory analysis — without a forward pass, without torch, and without a GPU.
$ metrik explore ./models/TinyLlama-1.1B
Reads safetensors headers (a length-prefixed JSON blob) and config.json. No tensor is ever
deserialized, no torch is imported, and no forward pass happens.
One honest caveat: ModelFingerprint.weight_digest is a merkle over the weight files, because
identity has to be content-based — a benchmark result attached to a name rather than a digest
is unfalsifiable (artifacts.md §6.1). So the command does
stream every weight byte through BLAKE3 once. It is I/O-bound, not compute-bound, and nothing
is held in memory, but it is not free on a 140 GB directory. Header parsing alone is free,
and that is what the structural analysis uses.
Emits four artifacts:
| Artifact | What it carries |
|---|---|
ModelFingerprint |
identity: merkle over weight files, config digest, param split |
ModelGraph |
one node per tensor — no edges, declared as an omission |
GraphSummary |
role/dtype/depth breakdown plus evidenced observations |
MemoryEstimate |
analytic weights + KV cache, with stated assumptions |
What it deliberately does not do
- No
ParamStats. Per-tensor statistics need the weight data and a blob format that is still an open question (artifacts.md§8, question 1). - No graph edges. Headers describe storage, not dataflow. A topology needs torch and a forward pass, which is a different loader behind the same contract.
- No measured numbers.
MemoryEstimateis analytic by type and can never contain one (artifacts.md§5).
Role classification is name-based pattern matching, so GraphSummary.classification_confidence
always reports what fraction of parameters it could not classify. A confident breakdown of a
model the heuristic did not understand is worse than no breakdown.
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