Model page

ApolloRaines/DeepSeek-R1-Distill-Qwen-7B-Desyced warn

Its license differs from its base model's; glitch tokens that can silently corrupt ordinary input.

downloads 737likes 0license otherarch qwen2params 7615.6Mupdated 2026-09-19

claims base: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B · chat template: present · view on Hugging Face ↗

Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batteryfaileddetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-21152,064-token embedding scanned · 14086 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/ApolloRaines/DeepSeek-R1-Distill-Qwen-7B-Desyced' | Traceback (most recent call last): | raise _format(HfHubHTTPError, message, response) from e | huggingface_hub.errors.HfHubHTTPError: (Request ID: Root=1-6a8e1644-69089d8f0881c1d22d1f2148;8ce138d5-ce49-4107-8963-38a92ab88945)

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

Findings

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

medium License differs from claimed parent (other vs mit)

This model declares other while its claimed base deepseek-ai/DeepSeek-R1-Distill-Qwen-7B declares mit. Verify the re-license is permitted before commercial use.

How to fix

Verify the re-license is actually permitted before relying on it.

  1. Read the parent's license for derivative-work and re-licensing terms — many open-weight licenses (e.g. Llama-family) do not permit arbitrary re-licensing.
  2. If the re-license is not permitted, the parent's terms govern your use regardless of what this repo declares.

medium Undertrained (glitch) token surface in vocabulary

Embedding-norm scan flagged 14086 undertrained tokens (norm < 0.3× the vocabulary median of 1.160), including 3462 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "$PostalCodesNL", "<unk>", "useRalative", "useRal", "Cumhurba". 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 deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Mean cosine similarity of 64 sampled token-embedding rows against deepseek-ai/DeepSeek-R1-Distill-Qwen-7B 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.

Put this result in your workflow

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Fingerprint

The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-21.

architectureqwen2 · 28 layers · 3584-dim
parameters7615.6M
vocabulary152,064 tokens
licenseother
serializationsafetensors + gguf
chat templatepresent · sha256:56a1447ad31926fd
claimed lineagedeepseek-ai/DeepSeek-R1-Distill-Qwen-7B
lineage verifiedconsistent vs deepseek-ai/DeepSeek-R1-Distill-Qwen-7B — embedding-row cosine 1.000
glitch-token surface14,086 undertrained candidates, 3,462 plain-ASCII
Full measured fingerprint
architecturesQwen2ForCausalLM
pipelinetext-generation
repo files15
revisionbeb07517c1e2
HF snapshot516 downloads · 0 likes · updated 2026-08-04 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 152,064×3584
embedding normsmedian 1.1598 · mean 1.0362
lineage checkconsistent — cosine 1 over 64 sampled rows vs deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
glitch-token samples"TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "$PostalCodesNL", "<unk>", "useRalative", "useRal", "Cumhurba", "webElementX", "NdrFc", "_ComCallableWrapper", "NdrFcShort"
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-21 05:3045s1
weights run 2026-08-21 — measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 152,064×3584
glitch surface14,086 undertrained, 3,462 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Verdict badge

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Ingot verdict: warn

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