Model page

DiTy/gemma-2-9b-it-russian-function-calling-GGUF warn

Its license differs from its base model's; the chat template differs from its base model, which changes behavior.

Could not load this model from the Hugging Face API (private, gated, or nonexistent). Findings below are from our archive.

Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-21
Weights batteryWeights forensics — no GPU, no downloadcomplete 2026-08-21256,000-token embedding scanned · 0 undertrained · lineage consistent
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.

medium License differs from claimed parent (apache-2.0 vs gemma)

This model declares apache-2.0 while its claimed base google/gemma-2-9b-it declares gemma. Verify the re-license is permitted before commercial use.

How to fix

Verify the re-license is actually permitted before relying on it.

  1. Read the parent's license for derivative-work and re-licensing terms — many open-weight licenses (e.g. Llama-family) do not permit arbitrary re-licensing.
  2. If the re-license is not permitted, the parent's terms govern your use regardless of what this repo declares.

medium Chat template differs from claimed parent

The chat template does not match google/gemma-2-9b-it's. Template drift silently changes model behavior even when weights are identical — 37% of drifted derivatives in our census left it undisclosed. Diff the templates before deploying.

How to fixingot patch

Restore the parent's chat template in `tokenizer_config.json` — a pure metadata fix.

  1. Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
  2. 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.
  3. 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.

info Embedding-norm glitch scan clean

No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (1.701). The glitch-token data-corruption class has no candidate surface in this model.

info Weights consistent with claimed parent google/gemma-2-9b-it

Mean cosine similarity of 64 sampled token-embedding rows against google/gemma-2-9b-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.

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 DiTy/gemma-2-9b-it-russian-function-calling-GGUF

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.

architecturegemma2 · 42 layers · 3584-dim
parameters9241.7M
vocabulary256,000 tokens
licenseapache-2.0
serializationsafetensors + gguf
chat templatepresent · sha256:68613177732f6daa
claimed lineagegoogle/gemma-2-9b-it
lineage verifiedconsistent vs google/gemma-2-9b-it — embedding-row cosine 1.000
glitch-token surfaceclean no undertrained tokens
Full measured fingerprint
architecturesGemma2ForCausalLM
librarytransformers
pipelinetext-generation
repo files14
revision5d2490469e56
HF snapshot1.9k downloads · 30 likes · updated 2024-12-06 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 256,000×3584
embedding normsmedian 1.701 · mean 1.7215
lineage checkconsistent — cosine 1 over 64 sampled rows vs google/gemma-2-9b-it
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightscomplete2026-08-21 05:1187s1
weights run 2026-08-21 measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 256,000×3584
glitch surface0 undertrained, 0 plain-ASCII
lineage checkconsistent — cosine 1 over 64 rows vs google/gemma-2-9b-it

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Ingot verdict: warn

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