ByteDance-Seed/BAGEL-7B-MoT warn
Glitch tokens that can silently corrupt ordinary input; Glitch tokens confirmed behaviorally (echo test). Plus 1 minor note.
claims base: Qwen/Qwen2.5-7B-Instruct · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-27Weights battery2026-08-21Behavioral batterycompletedetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-27 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-21152,064-token embedding scanned · 15184 undertrained · lineage inconclusive |
| Behavioral battery | Live-inference differentials | completefull 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 15184 undertrained tokens (norm < 0.3× the vocabulary median of 3.022), including 757 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "thuisontvangst", "useRalative". In models where this class was tested behaviorally, such tokens silently rewrote user input into confident, schema-valid, wrong output. These are candidates from the weights alone; behavioral confirmation requires the behavioral battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
low Lineage vs claimed parent Qwen/Qwen2.5-7B-Instruct inconclusive
Mean embedding-row cosine similarity to the declared base is 0.568 — below the 0.8 typical of true derivatives but not low enough to call mislabeled. Heavy continued pretraining or vocabulary surgery can look like this; verify provenance before relying on the parent's safety or licensing posture.
How to fix
Fix or verify the `base_model` declaration so lineage checks can run.
- If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
- If you don't: identify the true parent (config architecture + weight shapes narrow it fast) and re-scan with that lineage in mind.
medium Glitch tokens confirmed behaviorally (echo test)
Asked to repeat its own undertrained tokens verbatim, the model failed on 12/16 while repeating 7/8 matched normal tokens correctly — e.g. "PostalCodesNL" → "我无法重复这个字符串，因为您提供的字符串包含中文字符，而我无法识别或处理这些字符"; "$PostalCodesNL" → "$"; "ForCanBeConverted" → "For". These strings, appearing in input as identifiers (usernames, SKUs, error codes), are rewritten silently. Greedy decoding, temperature 0, seed 0.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
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Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-21.
| architecture | bagel |
| parameters | 14691.1M |
| vocabulary | 152,064 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | present · sha256:cd8e9439f0570856 |
| claimed lineage | Qwen/Qwen2.5-7B-Instruct |
| lineage verified | inconclusive vs Qwen/Qwen2.5-7B-Instruct — embedding-row cosine 0.568 |
| glitch-token surface | 15,184 undertrained candidates, 757 plain-ASCII |
Full measured fingerprint
| architectures | BagelForConditionalGeneration |
| library | bagel-mot |
| pipeline | any-to-any |
| repo files | 14 |
| revision | 5019f57d168e |
| HF snapshot | 670 downloads · 1.2k likes · updated 2026-01-09 · captured 2026-08-21 |
| embedding tensor | language_model.model.embed_tokens.weight · BF16 · 152,064×3584 |
| embedding norms | median 3.0221 · mean 2.6575 |
| lineage check | inconclusive — cosine 0.568 over 64 sampled rows vs Qwen/Qwen2.5-7B-Instruct |
| glitch-token samples | "TokenNameIdentifier", "ForCanBeConverted", "ForCanBeConvertedToF", "PostalCodesNL", "$PostalCodesNL", "<unk>", "thuisontvangst", "useRalative", "useRal", "prostituerte", "NdrFc", "_ComCallableWrapper" |
Battery runs (2)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| gpu | complete | 2026-08-27 04:31 | 73s | 1 |
| weights | complete | 2026-08-21 05:13 | 71s | 1 |
gpu run 2026-08-27 — measurements
| probes run | glitch |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | language_model.model.embed_tokens.weight · BF16 · 152,064×3584 |
| glitch surface | 15,184 undertrained, 757 plain-ASCII |
| lineage check | inconclusive — cosine 0.568 over 64 rows vs Qwen/Qwen2.5-7B-Instruct |
Verdict badge
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