alphaedge-ai/gemma-3-4b-it-mya-16384 pass
claims base: google/gemma-3-4b-it · 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 2026-08-21 |
| 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 |
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.
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
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 05:16 | 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)