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Qwen/Qwen2.5-7B-Instruct warn

Glitch tokens that can silently corrupt ordinary input. Plus 1 minor note.

downloads 8.4Mlikes 2.4klicense apache-2.0arch qwen2params 7615.6Mupdated 2025-01-12

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

Scan coverageStatic battery2026-08-21Weights battery2026-08-20Behavioral batteryfaileddetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-20152,064-token embedding scanned · 7359 undertrained · lineage consistent
Behavioral batteryLive-inference differentialsfailedTraceback (most recent call last): | raise HTTPStatusError(message, request=request, response=self) | httpx.HTTPStatusError: Client error '429 Too Many Requests' for url 'https://huggingface.co/api/models/Qwen/Qwen2.5-7B-Instruct' | Traceback (most recent call last): | raise _format(HfHubHTTPError, message, response) from e | huggingface_hub.errors.HfHubHTTPError: (Request ID: Root=1-6a8e107b-604345f41814866676c61d25;d01c9d4d-98f7-40ed-9e57-595774ac8b5c)

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

Findings

Scanned 2026-08-21 · published from a community scan. Full write-up: read the report.

medium Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 7359 undertrained tokens (norm < 0.3× the vocabulary median of 0.859), including 188 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "thuisontvangst". 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.

info Weights consistent with claimed parent Qwen/Qwen2.5-7B

Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen2.5-7B is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).

low Partial glitch-token echo degradation

Echo failures on 6/16 undertrained tokens vs 0/8 controls — a differential exists but below the confirmation bar (≥50% glitch failures with clean controls).

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.

Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.

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The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-20.

architectureqwen2 · 28 layers · 3584-dim
parameters7615.6M
vocabulary152,064 tokens
licenseapache-2.0
serializationsafetensors
chat templatepresent · sha256:cd8e9439f0570856
claimed lineageQwen/Qwen2.5-7B
lineage verifiedconsistent vs Qwen/Qwen2.5-7B — embedding-row cosine 1.000
glitch-token surface7,359 undertrained candidates, 188 plain-ASCII
Full measured fingerprint
architecturesQwen2ForCausalLM
librarytransformers
pipelinetext-generation
repo files14
revisiona09a35458c70
HF snapshot12.1M downloads · 1.5k likes · updated 2025-01-12 · captured 2026-08-20
embedding tensormodel.embed_tokens.weight · BF16 · 152,064×3584
embedding normsmedian 0.8588 · mean 0.7907
lineage checkconsistent — cosine 0.9999 over 64 sampled rows vs Qwen/Qwen2.5-7B
glitch-token samples"TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "thuisontvangst", "useRalative", "useRal", "prostituerte", "Cumhurba"
Battery runs (3)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
gpucomplete2026-08-21 08:0649s1
weightscomplete2026-08-20 18:0268s1
weightscomplete2026-08-20 08:1068s1
gpu run 2026-08-21 — measurements
probes runglitch

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Ingot verdict: warn

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