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Qwen/Qwen3.6-27B-FP8 warn

Glitch tokens that can silently corrupt ordinary input.

downloads 3.6Mlikes 360license apache-2.0arch qwen3_5params 27782.9Mupdated 2026-04-24

claims base: Qwen/Qwen3.6-27B · chat template: present · view on Hugging Face ↗

Scan coverageStatic battery2026-08-20Weights battery2026-08-20Behavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-20
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-20248,320-token embedding scanned · 1718 undertrained · lineage consistent
Behavioral batteryLive-inference differentialsnot run

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

Findings

Scanned 2026-08-20 · published from a community scan.

medium Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 1718 undertrained tokens (norm < 0.3× the vocabulary median of 0.890), including 438 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "tedothi", "ForCanBeConvertedToF", "szexf", "ForCanBeConverted", "xfabl", "Kinhted", "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/Qwen3.6-27B

Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen3.6-27B 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 2026-08-20.

architectureqwen3_5
parameters27782.9M
vocabulary248,320 tokens
licenseapache-2.0
serializationsafetensors
chat templatepresent · sha256:e84f32a23fdda276
claimed lineageQwen/Qwen3.6-27B
lineage verifiedconsistent vs Qwen/Qwen3.6-27B — embedding-row cosine 1.000
glitch-token surface1,718 undertrained candidates, 438 plain-ASCII
Full measured fingerprint
architecturesQwen3_5ForConditionalGeneration
librarytransformers
pipelineimage-text-to-text
repo files80
revisione89b16ebf198
HF snapshot9.4M downloads · 347 likes · updated 2026-04-24 · captured 2026-08-20
embedding tensormodel.language_model.embed_tokens.weight · BF16 · 248,320×5120
embedding normsmedian 0.8902 · mean 0.8594
lineage checkconsistent — cosine 1 over 64 sampled rows vs Qwen/Qwen3.6-27B
glitch-token samples"tedothi", "ForCanBeConvertedToF", "szexf", "ForCanBeConverted", "xfabl", "Kinhted", "PostalCodesNL", "useRalative", "tarsker", "useRal", "ejahter", "tarskereso"
Battery runs (3)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-20 18:0280s1
weightscomplete2026-08-20 08:1071s1
weightscomplete2026-08-20 06:332m1

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Ingot verdict: warn

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