Video-R1/Qwen2.5-VL-7B-COT-SFT warn
The chat template differs from its base model, which changes behavior; glitch tokens that can silently corrupt ordinary input.
claims base: Qwen/Qwen2.5-7B-Instruct · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-21 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-21152,064-token embedding scanned · 5768 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-21 · published from a community scan.
medium Chat template differs from claimed parent
The chat template does not match Qwen/Qwen2.5-7B-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 5768 undertrained tokens (norm < 0.3× the vocabulary median of 0.948), including 127 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "useRalative", "TokenNameIdentifier". 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.
info Weights consistent with claimed parent Qwen/Qwen2.5-7B-Instruct
Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen2.5-7B-Instruct is 0.801 — 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 Video-R1/Qwen2.5-VL-7B-COT-SFT
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 | qwen2_5_vl · 28 layers · 3584-dim |
| parameters | 8292.2M |
| vocabulary | 152,064 tokens |
| license | apache-2.0 |
| serialization | safetensors pickle |
| chat template | present · sha256:44d5f08f3f72b837 |
| claimed lineage | Qwen/Qwen2.5-7B-Instruct |
| lineage verified | consistent vs Qwen/Qwen2.5-7B-Instruct — embedding-row cosine 0.801 |
| glitch-token surface | 5,768 undertrained candidates, 127 plain-ASCII |
Full measured fingerprint
| architectures | Qwen2_5_VLForConditionalGeneration |
| library | transformers |
| pipeline | video-text-to-text |
| repo files | 19 — pickle: training_args.bin |
| revision | f71f0f1e22c0 |
| HF snapshot | 649 downloads · 2 likes · updated 2025-10-23 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 152,064×3584 |
| embedding norms | median 0.9478 · mean 0.8827 |
| lineage check | consistent — cosine 0.8008 over 64 sampled rows vs Qwen/Qwen2.5-7B-Instruct |
| glitch-token samples | "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "useRalative", "TokenNameIdentifier", "useRal", "thuisontvangst", "NdrFc", "Cumhurba" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 05:14 | 88s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 152,064×3584 |
| glitch surface | 5,768 undertrained, 127 plain-ASCII |
| lineage check | consistent — cosine 0.8008 over 64 rows vs Qwen/Qwen2.5-7B-Instruct |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/Video-R1/Qwen2.5-VL-7B-COT-SFT)