DiTy/gemma-2-9b-it-function-calling-GGUF warn
claims base: google/gemma-2-9b-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 2026-08-21256,000-token embedding scanned · 0 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | not run |
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.
- 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.
- 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.
- Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
- 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.
- 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.
- 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 | gemma2 · 42 layers · 3584-dim |
| parameters | 9241.7M |
| vocabulary | 256,000 tokens |
| license | apache-2.0 |
| serialization | safetensors + gguf |
| chat template | present · sha256:e25323d08b07c2ce |
| claimed lineage | google/gemma-2-9b-it |
| lineage verified | consistent vs google/gemma-2-9b-it — embedding-row cosine 1.000 |
| glitch-token surface | clean no undertrained tokens |
Full measured fingerprint
| architectures | Gemma2ForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 14 |
| revision | f61b53a8f2e1 |
| HF snapshot | 486 downloads · 11 likes · updated 2024-12-06 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 256,000×3584 |
| embedding norms | median 1.701 · mean 1.7215 |
| lineage check | consistent — cosine 1 over 64 sampled rows vs google/gemma-2-9b-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 05:30 | 60s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 256,000×3584 |
| glitch surface | 0 undertrained, 0 plain-ASCII |
| lineage check | consistent — cosine 1 over 64 rows vs google/gemma-2-9b-it |
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-function-calling-GGUF
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/DiTy/gemma-2-9b-it-function-calling-GGUF)