Model page

bytedance-research/pasa-7b-selector warn

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

downloads 188likes 9license cc-by-nc-sa-4.0arch qwen2params 7615.6Mupdated 2025-05-13

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

Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-21152,064-token embedding scanned · 7359 undertrained · lineage consistent
Behavioral batteryLive-inference differentialsnot run

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 (cc-by-nc-sa-4.0 vs apache-2.0)

This model declares cc-by-nc-sa-4.0 while its claimed base Qwen/Qwen2.5-7B-Instruct declares apache-2.0. 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 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-Instruct

Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen2.5-7B-Instruct 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

Check every checkpoint before it ships

Use the web app, API, CLI, or CI gate to scan candidate checkpoints and catch model drift before deployment. Public-model scans publish to the open database; paid plans add the volume needed for continuous checks.

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
licensecc-by-nc-sa-4.0
serializationsafetensors pickle
chat templatepresent · sha256:cd8e9439f0570856
claimed lineageQwen/Qwen2.5-7B-Instruct
lineage verifiedconsistent vs Qwen/Qwen2.5-7B-Instruct — embedding-row cosine 1.000
glitch-token surface7,359 undertrained candidates, 188 plain-ASCII
Full measured fingerprint
architecturesQwen2ForCausalLM
pipelinetext-generation
repo files10 — pickle: pytorch_model.bin
revision862b9fc2f7b5
HF snapshot459 downloads · 9 likes · updated 2025-05-13 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 152,064×3584
embedding normsmedian 0.8588 · mean 0.7907
lineage checkconsistent — cosine 1 over 64 sampled rows vs Qwen/Qwen2.5-7B-Instruct
glitch-token samples"TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "(stypy", "thuisontvangst", "useRalative", "useRal", "prostituerte", "Cumhurba"
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-21 05:3165s1
weights run 2026-08-21 — measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 152,064×3584
glitch surface7,359 undertrained, 188 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs Qwen/Qwen2.5-7B-Instruct

Verdict badge

Ship the verdict in your README — it always shows the latest published analysis:

Ingot verdict: warn

[![Ingot scan](https://ingot.tools/api/v1/models/bytedance-research/pasa-7b-selector/badge.svg)](https://ingot.tools/models/bytedance-research/pasa-7b-selector)
Gate it in CI