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QuantTrio/Qwen3.5-9B-AWQ warn

Glitch tokens that can silently corrupt ordinary input.

downloads 652.0klikes 28license apache-2.0arch qwen3_5params 9653.1Mupdated 2026-03-04

claims base: Qwen/Qwen3.5-9B · chat template: present · view on Hugging Face ↗

Scan coverageStatic battery2026-08-22Weights battery2026-08-22Behavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-22
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-22248,320-token embedding scanned · 1585 undertrained · lineage consistent
Behavioral batteryLive-inference differentialsnot run

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

Findings

Scanned 2026-08-22 · published from a community scan.

medium Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 1585 undertrained tokens (norm < 0.3× the vocabulary median of 0.886), including 397 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "tedothi", "ForCanBeConvertedToF", "ForCanBeConverted", "szexf", "Kinhted", "xfabl", "PostalCodesNL", "useRalative". 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/Qwen3.5-9B

Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen3.5-9B is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).

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

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Fingerprint

The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-22.

architectureqwen3_5
parameters9653.1M
vocabulary248,320 tokens
licenseapache-2.0
serializationsafetensors
chat templatepresent · sha256:a4aee8afcf2e0711
claimed lineageQwen/Qwen3.5-9B
lineage verifiedconsistent vs Qwen/Qwen3.5-9B — embedding-row cosine 1.000
glitch-token surface1,585 undertrained candidates, 397 plain-ASCII
Full measured fingerprint
architecturesQwen3_5ForConditionalGeneration
librarytransformers
pipelineimage-text-to-text
repo files22
revision938f8e3ef86c
HF snapshot1.2M downloads · 25 likes · updated 2026-03-04 · captured 2026-08-21
embedding tensormodel.language_model.embed_tokens.weight · BF16 · 248,320×4096
embedding normsmedian 0.8862 · mean 0.8524
lineage checkconsistent — cosine 1 over 64 sampled rows vs Qwen/Qwen3.5-9B
glitch-token samples"tedothi", "ForCanBeConvertedToF", "ForCanBeConverted", "szexf", "Kinhted", "xfabl", "PostalCodesNL", "useRalative", "tarsker", "tarskereso", "ejahter", "useRal"
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-21 07:432m1
weights run 2026-08-21 — measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.language_model.embed_tokens.weight · BF16 · 248,320×4096
glitch surface1,585 undertrained, 397 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs Qwen/Qwen3.5-9B

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Ingot verdict: warn

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