numind/NuExtract3 warn
EOS ids disjoint between config.json and generation_config.json; the chat template differs from its base model, which changes behavior.
claims base: Qwen/Qwen3.5-4B · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-27Weights battery2026-08-27Behavioral batteryn/adetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-27 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-27248,320-token embedding scanned · 0 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | n/anot applicable: image-to-text model has no text-generation surface to probe |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-27 · published from a community scan.
medium EOS ids disjoint between config.json and generation_config.json
config.json declares eos_token_id [248046] while generation_config.json declares [248044] with no overlap. Runtimes read one or the other, so at least one of them stops generation on the wrong token (or never). Align both files on the token the chat template actually ends turns with.
How to fixingot patch
Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).
- Identify the token the chat template actually ends assistant turns with (e.g. `<|eot_id|>`, `<end_of_turn>`, `<|im_end|>`) and make sure its id is in `generation_config.json`'s `eos_token_id` list.
- Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
- For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
- Until the repo is fixed, pass explicit stop tokens to your serving stack (e.g. vLLM `stop_token_ids`, llama.cpp `--override-kv tokenizer.ggml.eos_token_id`).
medium Chat template differs from claimed parent
The chat template does not match Qwen/Qwen3.5-4B'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.
info Partial coverage — not a generative language model
image-to-text model — no token vocabulary, so chat-template, tokenizer, and behavioral checks are not applicable; packaging, license, and serialization (pickle) checks apply.
info Embedding-norm glitch scan clean
No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (0.652). The glitch-token data-corruption class has no candidate surface in this model.
info Weights consistent with claimed parent Qwen/Qwen3.5-4B
Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen3.5-4B is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).
Check every checkpoint before it ships
Use the web app, API, CLI, or CI gate to scan candidate checkpoints and catch model drift before deployment. Public-model scans publish to the open database; paid plans add the volume needed for continuous checks.
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/NuExtract3
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 2026-08-27.
| architecture | qwen3_5 |
| parameters | 4539.3M |
| vocabulary | 248,320 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | present (chat_template.jinja) · sha256:31e44d28615d268e |
| claimed lineage | Qwen/Qwen3.5-4B |
| lineage verified | consistent vs Qwen/Qwen3.5-4B — embedding-row cosine 1.000 |
| glitch-token surface | clean no undertrained tokens |
Full measured fingerprint
| architectures | Qwen3_5ForConditionalGeneration |
| library | transformers |
| pipeline | image-to-text |
| repo files | 29 |
| revision | c99dc8f5641b |
| HF snapshot | 127.0k downloads · 344 likes · updated 2026-08-20 · captured 2026-08-27 |
| embedding tensor | model.language_model.embed_tokens.weight · BF16 · 248,320×2560 |
| embedding norms | median 0.6518 · mean 0.6557 |
| lineage check | consistent — cosine 1 over 64 sampled rows vs Qwen/Qwen3.5-4B |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-27 22:27 | 66s | 1 |
weights run 2026-08-27 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, lineage-norm-correlation |
| embedding tensor | model.language_model.embed_tokens.weight · BF16 · 248,320×2560 |
| glitch surface | 0 undertrained, 0 plain-ASCII |
| lineage check | consistent — cosine 1 over 64 rows vs Qwen/Qwen3.5-4B |
Verdict badge
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[](https://ingot.tools/models/numind/NuExtract3)