NeuronUz/NeuronAI-Uzbek warn
The chat template was dropped from its base model, which changes behavior; its tokenizer differs from its claimed base model.
claims base: Qwen/Qwen3-4B · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-21 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-21180,000-token embedding scanned · 0 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-21 · published from a community scan.
medium Chat template dropped vs parent
Qwen/Qwen3-4B ships a chat template; this repo does not. Serving stacks will silently fall back to a generic template, changing behavior. (In our 296-model census, 78% of pure quantization re-releases changed or dropped the template.)
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 (180000 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 (1.150). The glitch-token data-corruption class has no candidate surface in this model.
info Weights consistent with claimed parent Qwen/Qwen3-4B
Mean cosine similarity of 54 sampled token-embedding rows against Qwen/Qwen3-4B is 0.999 — the weights plausibly descend from the declared base (relation: unspecified).
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.
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 NeuronUz/NeuronAI-Uzbek
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 2026-08-21.
| architecture | qwen3 · 36 layers · 2560-dim |
| parameters | 4094.3M |
| vocabulary | 180,000 tokens |
| license | apache-2.0 |
| serialization | safetensors pickle |
| chat template | none |
| claimed lineage | Qwen/Qwen3-4B |
| lineage verified | consistent vs Qwen/Qwen3-4B — embedding-row cosine 0.999 |
| glitch-token surface | clean no undertrained tokens |
Full measured fingerprint
| architectures | Qwen3ForCausalLM |
| pipeline | text-generation |
| repo files | 17 — pickle: training_args.bin |
| revision | e53811493580 |
| HF snapshot | 252 downloads · 7 likes · updated 2026-01-21 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 180,000×2560 |
| embedding norms | median 1.15 · mean 1.1897 |
| lineage check | consistent — cosine 0.9992 over 54 sampled rows vs Qwen/Qwen3-4B |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 05:16 | 79s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 180,000×2560 |
| glitch surface | 0 undertrained, 0 plain-ASCII |
| lineage check | consistent — cosine 0.9992 over 54 rows vs Qwen/Qwen3-4B |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/NeuronUz/NeuronAI-Uzbek)