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Qwen/Qwen3-VL-8B-Instruct warn

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

downloads 14.6Mlikes 1.2klicense apache-2.0arch qwen3_vlparams 8767.1Mupdated 2025-10-15

chat template: present · view on Hugging Face ↗

Scan coverageStatic battery2026-08-27Weights battery2026-08-20Behavioral batterycompletedetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-27
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-20151,936-token embedding scanned · 2943 undertrained
Behavioral batteryLive-inference differentialscompletefull differential battery (curated)

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

Findings

Scanned 2026-08-27 · published from a community scan.

medium Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 2943 undertrained tokens (norm < 0.3× the vocabulary median of 1.396), including 100 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "$PostalCodesNL", "ForCanBeConvertedToF", "PostalCodesNL", "ForCanBeConverted", "useRalative", "useRal", "NdrFc", "sexkontakte". 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 Partial glitch-token echo degradation

Echo failures on 5/16 undertrained tokens vs 0/8 controls — a differential exists but below the confirmation bar (≥50% glitch failures with clean controls).

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.

architectureqwen3_vl
parameters8767.1M
vocabulary151,936 tokens
licenseapache-2.0
serializationsafetensors
chat templatepresent · sha256:3636d0f0bd6bef02
glitch-token surface2,943 undertrained candidates, 100 plain-ASCII
Full measured fingerprint
architecturesQwen3VLForConditionalGeneration
librarytransformers
pipelineimage-text-to-text
repo files16
revision0c351dd01ed8
HF snapshot5.3M downloads · 1.1k likes · updated 2025-10-15 · captured 2026-08-20
embedding tensormodel.language_model.embed_tokens.weight · BF16 · 151,936×4096
embedding normsmedian 1.3956 · mean 1.3226
lineage checkno claimed base model
glitch-token samples"$PostalCodesNL", "ForCanBeConvertedToF", "PostalCodesNL", "ForCanBeConverted", "useRalative", "useRal", "NdrFc", "sexkontakte", "webElementX", "davidjl", "sextreffen", "-vesm"
Battery runs (4)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
gpucomplete2026-08-27 04:312m1
weightscomplete2026-08-20 18:0229s1
weightscomplete2026-08-20 08:1026s1
weightscomplete2026-08-20 06:3539s1
gpu run 2026-08-27 — measurements
probes runglitch

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Ingot verdict: warn

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