RefalMachine/RuadaptQwen3-4B-Hybrid warn
claims base: Qwen/Qwen3-4B · chat template: present · view on Hugging Face ↗
Scan coverage
Ingot runs three batteries against a model. What each one checks →
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-22 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-22146,260-token embedding scanned · 0 undertrained · lineage inconclusive |
| Behavioral battery | Live-inference differentials | not run |
Findings
Scanned 2026-08-22 · published from a community scan.
medium Chat template differs from claimed parent
The chat template does not match Qwen/Qwen3-4B's. Template drift silently changes model behavior even when weights are identical — 37% of drifted derivatives in our census left it undisclosed. Diff the templates before deploying.
How to fixingot patch
Restore the parent's chat template in `tokenizer_config.json` — a pure metadata fix.
- Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
- Or fix by hand: copy the `chat_template` value from the parent repo's `tokenizer_config.json` into this model's, and pin your serving stack to that file.
- If the drift was intentional (the author retrained on a new template), confirm that in the model card before "fixing" it — restoring the parent template on retrained weights changes behavior too.
medium Vocabulary size differs from claimed parent (146260 vs 151936)
A changed vocab means changed tokenization: strings will split differently than on Qwen/Qwen3-4B, 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 (1.142). The glitch-token data-corruption class has no candidate surface in this model.
low Lineage vs claimed parent Qwen/Qwen3-4B inconclusive
Mean embedding-row cosine similarity to the declared base is 0.697 — 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.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-22.
| architecture | qwen3 · 36 layers · 2560-dim |
| parameters | 4007.9M |
| vocabulary | 146,260 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | present · sha256:87a2728cb8dc9fe4 |
| claimed lineage | Qwen/Qwen3-4B |
| lineage verified | inconclusive vs Qwen/Qwen3-4B — embedding-row cosine 0.697 |
| glitch-token surface | clean no undertrained tokens |
Full measured fingerprint
| architectures | Qwen3ForCausalLM |
| repo files | 13 |
| revision | bb890732923b |
| HF snapshot | 352 downloads · 4 likes · updated 2025-08-26 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 146,260×2560 |
| embedding norms | median 1.1422 · mean 1.1266 |
| lineage check | inconclusive — cosine 0.6972 over 64 sampled rows vs Qwen/Qwen3-4B |
Battery runs
The run trace behind the findings above: every deep-battery job for this model, with what each run measured or why it failed. Findings are only as good as the runs that produced them.
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 05:32 | 58s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 146,260×2560 |
| glitch surface | 0 undertrained, 0 plain-ASCII |
| lineage check | inconclusive — cosine 0.6972 over 64 rows vs Qwen/Qwen3-4B |
Fix it
Some findings are metadata-level and patchable — apply the fixes to your local copy (your weights never leave your machine):
npx @ingotai/scan patch RefalMachine/RuadaptQwen3-4B-Hybrid
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/RefalMachine/RuadaptQwen3-4B-Hybrid)