Qwen/Qwen3-Embedding-0.6B warn
claims base: Qwen/Qwen3-0.6B-Base · chat template: present · view on Hugging Face ↗
Ingot findings
Static battery: 1 medium finding(s). Deep battery (behavioral differential, glitch-token pass) not yet run. Weights battery: embedding-norm scan over 151669 tokens (BF16, 1024-dim) found 0 undertrained candidates, 0 plain-ASCII. Lineage vs Qwen/Qwen3-0.6B-Base: consistent. Scanned 2026-08-20 (published from a community scan).
medium Vocabulary size differs from claimed parent (151669 vs 151936)
A changed vocab means changed tokenization: strings will split differently than on Qwen/Qwen3-0.6B-Base, which can shift behavior on identifiers, codes, and non-English text.
How to fixweight-level
Not patchable: the vocab size mirrors the embedding matrix in the weights. Verify the change was intentional.
- Do not edit `vocab_size` in config.json to "match the parent" — it must equal the embedding table in the shipped weights or the model won't load.
- Diff the tokenizers (`tokenizer.json` / added_tokens) against the parent to see what was added or removed, and test your own identifiers, codes, and non-English text through both.
- If the drift is unexplained by the model card, treat tokenization-sensitive behavior as unvalidated on this model.
info Embedding-norm glitch scan clean
No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (0.968). The glitch-token data-corruption class has no candidate surface in this model.
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.
info Weights consistent with claimed parent Qwen/Qwen3-0.6B-Base
Mean cosine similarity of 64 sampled token-embedding rows against Qwen/Qwen3-0.6B-Base is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).
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.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable weights-and-metadata profile of this model, rebuilt on every scan and deep-battery run. Updated 2026-08-20.
| architecture | qwen3 · 28 layers · 1024-dim |
| parameters | 595.8M |
| vocabulary | 151,669 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | present · sha256:87a2728cb8dc9fe4 |
| claimed lineage | Qwen/Qwen3-0.6B-Base |
| lineage verified | consistent vs Qwen/Qwen3-0.6B-Base — embedding-row cosine 1.000 |
| glitch-token surface | clean no undertrained tokens |
Full fingerprint
| architectures | Qwen3ForCausalLM |
| library | sentence-transformers |
| pipeline | feature-extraction |
| repo files | 12 |
| revision | 97b0c614be4d |
| HF snapshot | 7.7M downloads · 1.2k likes · updated 2026-04-20 · captured 2026-08-20 |
| embedding tensor | embed_tokens.weight · BF16 · 151,669×1024 |
| embedding norms | median 0.9677 · mean 0.9565 |
| lineage check | consistent — cosine 1 over 64 sampled rows vs Qwen/Qwen3-0.6B-Base |
Verdict badge
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