semiotic/T5-3B-SynQL-KaggleDBQA-Train-Run-02 warn
Weights only ship in a format that can run code when loaded; its tokenizer differs from its claimed base model.
claims base: google-t5/t5-3b · chat template: not found · view on Hugging Face ↗
Scan coverageStatic battery2026-08-25Weights batteryqueuedBehavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-25 |
| Weights battery | Weights forensics — no GPU, no download | queued |
| 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 (optimizer.pt, pytorch_model.bin, rng_state.pth, …). 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 Vocabulary size differs from claimed parent (32102 vs 32128)
A changed vocab means changed tokenization: strings will split differently than on google-t5/t5-3b, 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.
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 | t5 · 24 layers · 1024-dim |
| vocabulary | 32,102 tokens |
| license | apache-2.0 |
| serialization | no safetensors pickle |
| chat template | none |
| claimed lineage | google-t5/t5-3b |
| lineage verified | unverified — weights battery pending |
Full measured fingerprint
| architectures | T5ForConditionalGeneration |
| library | transformers |
| pipeline | text-generation |
| repo files | 14 — pickle: optimizer.pt, pytorch_model.bin, rng_state.pth, scheduler.pt, training_args.bin |
| revision | 8b01a19d39ef |
| HF snapshot | 17 downloads · 0 likes · updated 2024-10-25 · captured 2026-08-25 |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | queued | 2026-08-25 22:21 | — | 0 |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/semiotic/T5-3B-SynQL-KaggleDBQA-Train-Run-02)