trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 warn
chat template: not in config · view on Hugging Face ↗
Ingot findings
Static battery: 1 medium finding(s). Deep battery (behavioral differential, glitch-token pass) not yet run. Weights battery: embedding-norm scan over 151665 tokens (BF16, 8-dim) found 48 undertrained candidates, 19 plain-ASCII. Scanned 2026-08-20 (published from a community scan).
medium No license declared
The model card declares no license. You have no usage rights by default — treat as all-rights-reserved until the owner clarifies.
How to fix
Get a license from the owner or pick a licensed alternative — this is a legal gap, not a technical one.
- With no declared license you have no usage rights by default; treat the weights as all-rights-reserved.
- Open an issue or discussion on the repo asking the owner to declare a license, or use the licensed upstream/parent model instead.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 48 undertrained tokens (norm < 0.3× the vocabulary median of 0.054), including 19 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "stems", "tracing", "fleet", "aters", ".pojo", "izard", "-centric", "integrates". In models where this class was tested behaviorally, such tokens silently rewrote user input into confident, schema-valid, wrong output. These are candidates from the weights alone; behavioral confirmation requires the GPU deep battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable weights-and-metadata profile of this model, rebuilt on every scan and deep-battery run. Updated 2026-08-20.
| architecture | qwen2 · 2 layers · 8-dim |
| parameters | 2.4M |
| vocabulary | 151,665 tokens |
| license | none declared |
| serialization | safetensors |
| chat template | none |
| glitch-token surface | 48 undertrained candidates, 19 plain-ASCII |
Full fingerprint
| architectures | Qwen2ForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 12 |
| revision | 4b10ebee6e13 |
| HF snapshot | 16.4M downloads · 20 likes · updated 2025-12-19 · captured 2026-08-20 |
| embedding tensor | model.embed_tokens.weight · BF16 · 151,665×8 |
| embedding norms | median 0.054 · mean 0.0546 |
| lineage check | no claimed base model |
| glitch-token samples | "stems", "tracing", "fleet", "aters", ".pojo", "izard", "-centric", "integrates", "stdout", "clud", "circle", "promo" |
Verdict badge
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