numind/NuExtract warn
claims base: microsoft/Phi-3-mini-4k-instruct · chat template: present · view on Hugging Face ↗
Scan coverage
Ingot runs three batteries against a model. What each one checks →
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-21 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-2132,064-token embedding scanned · 600 undertrained · lineage inconclusive |
| Behavioral battery | Live-inference differentials | not run |
Findings
Scanned 2026-08-21 · published from a community scan.
medium Repo ships executable Python (trust_remote_code)
The repository contains custom code files that run in-process when loaded with trust_remote_code=True. Pin the revision hash and review the code before loading.
How to fix
Review and pin the custom code; never float on `main` with trust_remote_code=True.
- Read every `.py` file in the repo before first load — this code runs in your process.
- Pin the revision: `from_pretrained(model_id, revision="<commit sha>", trust_remote_code=True)` so a later push can't swap the code under you.
- Prefer a version of the architecture already in `transformers` if one exists, which removes the remote-code requirement entirely.
medium Chat template differs from claimed parent
The chat template does not match microsoft/Phi-3-mini-4k-instruct's. Template drift silently changes model behavior even when weights are identical — 37% of drifted derivatives in our census left it undisclosed. Diff the templates before deploying.
How to fixingot patch
Restore the parent's chat template in `tokenizer_config.json` — a pure metadata fix.
- Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
- Or fix by hand: copy the `chat_template` value from the parent repo's `tokenizer_config.json` into this model's, and pin your serving stack to that file.
- If the drift was intentional (the author retrained on a new template), confirm that in the model card before "fixing" it — restoring the parent template on retrained weights changes behavior too.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 600 undertrained tokens (norm < 0.3× the vocabulary median of 1.650), including 99 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "<0xFC>", "Mediabestanden", "<0xFB>", "autorytatywna", "<0xFF>", "<0xFD>", "<0xFA>", "<0xFE>". In models where this class was tested behaviorally, such tokens silently rewrote user input into confident, schema-valid, wrong output. These are candidates from the weights alone; behavioral confirmation requires the behavioral battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
low Lineage vs claimed parent microsoft/Phi-3-mini-4k-instruct inconclusive
Mean embedding-row cosine similarity to the declared base is 0.745 — below the 0.8 typical of true derivatives but not low enough to call mislabeled. Heavy continued pretraining or vocabulary surgery can look like this; verify provenance before relying on the parent's safety or licensing posture.
How to fix
Fix or verify the `base_model` declaration so lineage checks can run.
- If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
- If you don't: identify the true parent (config architecture + weight shapes narrow it fast) and re-scan with that lineage in mind.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-21.
| architecture | phi3 · 32 layers · 3072-dim |
| parameters | 3821.1M |
| vocabulary | 32,064 tokens |
| license | mit |
| serialization | safetensors custom code |
| chat template | present · sha256:268b6082ceb7176d |
| claimed lineage | microsoft/Phi-3-mini-4k-instruct |
| lineage verified | inconclusive vs microsoft/Phi-3-mini-4k-instruct — embedding-row cosine 0.745 |
| glitch-token surface | 600 undertrained candidates, 99 plain-ASCII |
Full measured fingerprint
| architectures | Phi3ForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 16 |
| revision | cc8190e34b19 |
| HF snapshot | 504 downloads · 236 likes · updated 2026-05-19 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · F32 · 32,064×3072 |
| embedding norms | median 1.6499 · mean 1.5585 |
| lineage check | inconclusive — cosine 0.745 over 64 sampled rows vs microsoft/Phi-3-mini-4k-instruct |
| glitch-token samples | "<0xFC>", "Mediabestanden", "<0xFB>", "autorytatywna", "<0xFF>", "<0xFD>", "<0xFA>", "<0xFE>", "Webachiv", "regnigaste", "Genomsnitt", "Genomsnittlig" |
Battery runs
The run trace behind the findings above: every deep-battery job for this model, with what each run measured or why it failed. Findings are only as good as the runs that produced them.
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 05:30 | 45s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · F32 · 32,064×3072 |
| glitch surface | 600 undertrained, 99 plain-ASCII |
| lineage check | inconclusive — cosine 0.745 over 64 rows vs microsoft/Phi-3-mini-4k-instruct |
Fix it
Some findings are metadata-level and patchable — apply the fixes to your local copy (your weights never leave your machine):
npx @ingotai/scan patch numind/NuExtract
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/numind/NuExtract)