Model page

mergekit-community/Qwen3-7B-Instruct warn

downloads 301likes 2license none declaredarch qwen2params 7615.6Mupdated 2025-02-23

claims base: Qwen/Qwen2.5-Coder-7B-Instruct, Qwen/Qwen2.5-7B-Instruct, Qwen/Qwen2.5-Math-7B-Instruct · chat template: present · view on Hugging Face ↗

Scan coverage

Ingot runs three batteries against a model. What each one checks →

BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics — no GPU, no downloadcomplete 2026-08-21152,064-token embedding scanned · 8983 undertrained · lineage inconclusive
Behavioral batteryLive-inference differentialsnot run

Findings

Scanned 2026-08-21 · published from a community scan.

medium No license declared

The model card declares no license. You have no usage rights by default — treat as all-rights-reserved until the owner clarifies.

How to fix

Get a license from the owner or pick a licensed alternative — this is a legal gap, not a technical one.

  1. With no declared license you have no usage rights by default; treat the weights as all-rights-reserved.
  2. Open an issue or discussion on the repo asking the owner to declare a license, or use the licensed upstream/parent model instead.

medium Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 8983 undertrained tokens (norm < 0.3× the vocabulary median of 0.965), including 189 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "PostalCodesNL", "(stypy", "<unk>", "$PostalCodesNL", "TokenNameIdentifier", "Cumhurba", "wannonce", "NdrFc". 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.

  1. 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.
  2. 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.
  3. 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 Qwen/Qwen2.5-Coder-7B-Instruct inconclusive

Mean embedding-row cosine similarity to the declared base is 0.634 — 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.

  1. If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
  2. 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.

architectureqwen2 · 28 layers · 3584-dim
parameters7615.6M
vocabulary152,064 tokens
licensenone declared
serializationsafetensors
chat templatepresent · sha256:cd8e9439f0570856
claimed lineageQwen/Qwen2.5-Coder-7B-Instruct, Qwen/Qwen2.5-7B-Instruct, Qwen/Qwen2.5-Math-7B-Instruct
lineage verifiedinconclusive vs Qwen/Qwen2.5-Coder-7B-Instruct — embedding-row cosine 0.633
glitch-token surface8,983 undertrained candidates, 189 plain-ASCII
Full measured fingerprint
architecturesQwen2ForCausalLM
librarytransformers
pipelinetext-generation
repo files15
revision56adc8972416
HF snapshot332 downloads · 2 likes · updated 2025-02-23 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · F16 · 152,064×3584
embedding normsmedian 0.965 · mean 0.8848
lineage checkinconclusive — cosine 0.6335 over 64 sampled rows vs Qwen/Qwen2.5-Coder-7B-Instruct
glitch-token samples"PostalCodesNL", "(stypy", "<unk>", "$PostalCodesNL", "TokenNameIdentifier", "Cumhurba", "wannonce", "NdrFc", "prostituerte", "thuisontvangst", "aincontri", "bakeka"

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.

batterystatusqueueddurationattempts
weightscomplete2026-08-21 05:152m1
weights run 2026-08-21 measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · F16 · 152,064×3584
glitch surface8,983 undertrained, 189 plain-ASCII
lineage checkinconclusive — cosine 0.6335 over 64 rows vs Qwen/Qwen2.5-Coder-7B-Instruct

Verdict badge

Ship the verdict in your README — it always shows the latest published analysis:

Ingot verdict: warn

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