alphaedge-ai/gemma-3-4b-it-mya-16384 pass
No major issues. Minor: couldn't confirm it descends from its claimed base model.
claims base: google/gemma-3-4b-it · 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 2026-08-2116,384-token embedding scanned · 0 undertrained · lineage inconclusive |
| 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.
info Embedding-norm glitch scan clean
No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (1.007). The glitch-token data-corruption class has no candidate surface in this model.
low Lineage vs claimed parent google/gemma-3-4b-it inconclusive
Mean embedding-row cosine similarity to the declared base is 0.411 — 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.
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.
| architecture | gemma3 |
| parameters | 3670.8M |
| vocabulary | 16,384 tokens |
| license | gemma |
| serialization | safetensors |
| chat template | present · sha256:7de1c58e208eda46 |
| claimed lineage | google/gemma-3-4b-it |
| lineage verified | inconclusive vs google/gemma-3-4b-it — embedding-row cosine 0.411 |
| glitch-token surface | clean no undertrained tokens |
Full measured fingerprint
| architectures | Gemma3ForConditionalGeneration |
| library | transformers |
| pipeline | text-generation |
| repo files | 12 |
| revision | 3edb3600a3cc |
| HF snapshot | 237 downloads · 1 likes · updated 2026-05-20 · captured 2026-08-21 |
| embedding tensor | language_model.model.embed_tokens.weight · BF16 · 16,384×2560 |
| embedding norms | median 1.007 · mean 1.0071 |
| lineage check | inconclusive — cosine 0.4107 over 64 sampled rows vs google/gemma-3-4b-it |
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 | 30s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | language_model.model.embed_tokens.weight · BF16 · 16,384×2560 |
| glitch surface | 0 undertrained, 0 plain-ASCII |
| lineage check | inconclusive — cosine 0.4107 over 64 rows vs google/gemma-3-4b-it |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/alphaedge-ai/gemma-3-4b-it-mya-16384)