reaperdoesntknow/Qemma-sft warn
Weights only ship in a format that can run code when loaded; Chat template ends turns with <end_of_turn>, which is not a configured stop token; its license differs from its base model's. Plus 2 more issues.
claims base: google/gemma-3-1b-it, Qwen/Qwen3-0.6B · chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-25Weights battery2026-08-25Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-25 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-25262,149-token embedding scanned · 0 undertrained · lineage inconclusive · pickle audit clean |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-25 · published from a community scan.
medium Pickle-serialized weights, no safetensors
Weights ship only as pickle-based files (pytorch_model.bin, training_args.bin). Loading pickle executes arbitrary code from the file — prefer a safetensors release or load in a sandbox.
How to fix
Convert the weights to safetensors before loading them anywhere that matters.
- Do not load the pickle files in-process — pickle deserialization executes arbitrary code from the file.
- Convert locally in a sandbox: `pip install safetensors` and use `safetensors.torch.save_file` on a state dict loaded with `torch.load(..., weights_only=True)` (refuses most code-execution payloads), or use Hugging Face's `convert.py` space/script.
- Pin the exact revision hash you converted from, and load only your converted safetensors artifact from then on.
medium Chat template ends turns with <end_of_turn>, which is not a configured stop token
The chat template terminates assistant turns with <end_of_turn>, but the effective EOS set (config.json ∪ generation_config.json = [1] → ["<eos>"]) never stops on it. Config-honoring runtimes generate past the terminator until the token budget is exhausted — runaway cost and self-continuing fake turns. Add <end_of_turn>'s id to generation_config.json's eos_token_id.
How to fixingot patch
Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).
- Identify the token the chat template actually ends assistant turns with (e.g. `<|eot_id|>`, `<end_of_turn>`, `<|im_end|>`) and make sure its id is in `generation_config.json`'s `eos_token_id` list.
- Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
- For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
- Until the repo is fixed, pass explicit stop tokens to your serving stack (e.g. vLLM `stop_token_ids`, llama.cpp `--override-kv tokenizer.ggml.eos_token_id`).
medium License differs from claimed parent (osl-3.0 vs gemma)
This model declares osl-3.0 while its claimed base google/gemma-3-1b-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 Stop token dropped vs claimed parent but still used by the template
google/gemma-3-1b-it stops on [1,106]; this repo only stops on [1], losing <end_of_turn> (106) — which this repo's own chat template still emits. Config-honoring runtimes will generate straight past it. Restore the lost id(s) to generation_config.json's eos_token_id.
How to fixingot patch
Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).
- Identify the token the chat template actually ends assistant turns with (e.g. `<|eot_id|>`, `<end_of_turn>`, `<|im_end|>`) and make sure its id is in `generation_config.json`'s `eos_token_id` list.
- Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
- For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
- Until the repo is fixed, pass explicit stop tokens to your serving stack (e.g. vLLM `stop_token_ids`, llama.cpp `--override-kv tokenizer.ggml.eos_token_id`).
medium Vocabulary size differs from claimed parent (262149 vs 262144)
A changed vocab means changed tokenization: strings will split differently than on google/gemma-3-1b-it, which can shift behavior on identifiers, codes, and non-English text.
How to fixweight-level
Not patchable: the vocab size mirrors the embedding matrix in the weights. Verify the change was intentional.
- Do not edit `vocab_size` in config.json to "match the parent" — it must equal the embedding table in the shipped weights or the model won't load.
- Diff the tokenizers (`tokenizer.json` / added_tokens) against the parent to see what was added or removed, and test your own identifiers, codes, and non-English text through both.
- If the drift is unexplained by the model card, treat tokenization-sensitive behavior as unvalidated on this model.
info Embedding-norm glitch scan clean
No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (1.244). The glitch-token data-corruption class has no candidate surface in this model.
info Pickle static analysis clean
Opcode-level parse of pytorch_model.bin (no code executed) found only standard serialization globals (3 distinct, all torch/collections/numpy). Pickle remains an executable format — this verifies the current bytes, not future uploads; prefer a safetensors release.
low Lineage vs claimed parent google/gemma-3-1b-it inconclusive
Mean embedding-row cosine similarity to the declared base is 0.758 — 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.
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 reaperdoesntknow/Qemma-sft
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 2026-08-25.
| architecture | gemma3_text · 26 layers · 1152-dim |
| vocabulary | 262,149 tokens |
| license | osl-3.0 |
| serialization | no safetensors pickle |
| chat template | present (chat_template.jinja) · sha256:7de1c58e208eda46 |
| claimed lineage | google/gemma-3-1b-it, Qwen/Qwen3-0.6B |
| lineage verified | inconclusive vs google/gemma-3-1b-it — embedding-row cosine 0.758 |
| glitch-token surface | clean no undertrained tokens |
Full measured fingerprint
| architectures | Gemma3ForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 15 — pickle: pytorch_model.bin, training_args.bin |
| revision | f873068b4b54 |
| HF snapshot | 3.0k downloads · 0 likes · updated 2026-08-18 · captured 2026-08-25 |
| pickle audit | pytorch_model.bin — 3 standard global(s) |
| embedding tensor | model.embed_tokens.weight · F32 · 262,149×1152 |
| embedding norms | median 1.2435 · mean 1.2397 |
| lineage check | inconclusive — cosine 0.7585 over 64 sampled rows vs google/gemma-3-1b-it |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 21:46 | 81s | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, pickle-static-analysis, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · F32 · 262,149×1152 |
| glitch surface | 0 undertrained, 0 plain-ASCII |
| lineage check | inconclusive — cosine 0.7585 over 64 rows vs google/gemma-3-1b-it |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/reaperdoesntknow/Qemma-sft)