Model page

gghfez/gemma-3-4b-novision pass

downloads 843likes 7license gemmaarch gemma3_textparams 3880.3Mupdated 2025-03-15

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 →

BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics — no GPU, no downloadcomplete 2026-08-21262,208-token embedding scanned · 63 undertrained · lineage consistent
Behavioral batteryLive-inference differentialsnot run

Findings

Scanned 2026-08-21 · published from a community scan.

low Undertrained tokens in vocabulary (non-ASCII tail)

Embedding-norm scan flagged 63 undertrained tokens (norm < 0.3× the vocabulary median), but 0 decode to plain-ASCII strings, so exposure in English-language pipelines is limited. Behavioral confirmation requires the behavioral battery.

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.

  1. 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.
  2. 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.
  3. 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 google/gemma-3-4b-it

Mean cosine similarity of 64 sampled token-embedding rows against google/gemma-3-4b-it 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.

  1. If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
  2. 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.

architecturegemma3_text · 34 layers · 2560-dim
parameters3880.3M
vocabulary262,208 tokens
licensegemma
serializationsafetensors
chat templatepresent · sha256:7de1c58e208eda46
claimed lineagegoogle/gemma-3-4b-it
lineage verifiedconsistent vs google/gemma-3-4b-it — embedding-row cosine 1.000
glitch-token surface63 undertrained candidates, 0 plain-ASCII
Full measured fingerprint
architecturesGemma3ForCausalLM
librarytransformers
pipelinetext-generation
repo files12
revision23c1ca31428c
HF snapshot841 downloads · 7 likes · updated 2025-03-15 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 262,208×2560
embedding normsmedian 0.9986 · mean 0.9982
lineage checkconsistent — cosine 1 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.

batterystatusqueueddurationattempts
weightscomplete2026-08-21 05:1353s1
weights run 2026-08-21 measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 262,208×2560
glitch surface63 undertrained, 0 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs google/gemma-3-4b-it

Verdict badge

Ship the verdict in your README — it always shows the latest published analysis:

Ingot verdict: pass

[![Ingot scan](https://ingot.tools/api/v1/models/gghfez/gemma-3-4b-novision/badge.svg)](https://ingot.tools/models/gghfez/gemma-3-4b-novision)
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