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google/gemma-3-1b-it pass

No major issues. Minor: the weights diverge from the claimed base model.

downloads 3.2Mlikes 1.2klicense gemmaarch gemma3_textparams 999.9Mupdated 2025-04-04

claims base: google/gemma-3-1b-pt · chat template: present · view on Hugging Face ↗

Scan coverageStatic battery2026-08-21Weights battery2026-08-20Behavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics: no GPU, no downloadcomplete 2026-08-20262,144-token embedding scanned · 0 undertrained · lineage inconsistent
Behavioral batteryLive-inference differentialsnot run

Ingot runs three batteries against a model. What each one checks →

Findings

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

info Gated repository

Access requires accepting the owner's terms; check the gate conditions for redistribution and field-of-use limits.

How to fix

Read the gate terms before building on the model.

  1. Check the gate conditions on the Hugging Face repo for redistribution and field-of-use limits — they bind your deployment, not just your download.

info Embedding-norm glitch scan clean

No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (0.947). The glitch-token data-corruption class has no candidate surface in this model.

low Weights diverge from claimed parent google/gemma-3-1b-pt

This model declares google/gemma-3-1b-pt as its base (relation: unspecified), but mean cosine similarity of 64 sampled token-embedding rows against that parent is only 0.273 (true finetunes, merges, and quantizations sit above 0.8; independently trained weights sit near 0). Either the lineage label is wrong, or the model was so heavily re-trained, pruned, or distilled that the parent's properties (safety posture, evaluated behavior, licensing basis) should not be assumed to carry over. Verify provenance before relying on the parent's reputation.

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.

info Glitch-token echo probe: no candidate surface

Embedding-norm scan found no undertrained ASCII tokens (threshold 0.284 vs vocab median 0.9467), so the glitch-token data-corruption class has nothing to trigger on. Echo probe not applicable.

Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.

Put this result in your workflow

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Fingerprint

The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-20.

architecturegemma3_text
parameters999.9M
vocabulary262,144 tokens
licensegemma
serializationsafetensors
chat templatenone
claimed lineagegoogle/gemma-3-1b-pt
lineage verifiedinconsistent vs google/gemma-3-1b-pt — embedding-row cosine 0.273
glitch-token surfaceclean no undertrained tokens
Full measured fingerprint
architecturesGemma3ForCausalLM
librarytransformers
pipelinetext-generation
repo files10
gatedmanual
revisiondcc83ea841ab
HF snapshot5.1M downloads · 1.1k likes · updated 2025-04-04 · captured 2026-08-20
embedding tensormodel.embed_tokens.weight · BF16 · 262,144×1152
embedding normsmedian 0.9467 · mean 0.9475
lineage checkinconsistent — cosine 0.2728 over 64 sampled rows vs google/gemma-3-1b-pt
Battery runs (2)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
gpucomplete2026-08-21 08:0641s1
weightscomplete2026-08-20 18:0244s1
gpu run 2026-08-21 — measurements
probes runglitch

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Ingot verdict: pass

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