deepseek-ai/DeepSeek-OCR-2 warn
chat template: not found · 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 2026-08-22 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-22129,280-token embedding scanned · 843 undertrained |
| Behavioral battery | Live-inference differentials | not run |
Findings
Scanned 2026-08-22 · published from a community scan.
medium Repo ships executable Python (trust_remote_code)
The repository contains custom code files that run in-process when loaded with trust_remote_code=True. Pin the revision hash and review the code before loading.
How to fix
Review and pin the custom code; never float on `main` with trust_remote_code=True.
- Read every `.py` file in the repo before first load — this code runs in your process.
- Pin the revision: `from_pretrained(model_id, revision="<commit sha>", trust_remote_code=True)` so a later push can't swap the code under you.
- Prefer a version of the architecture already in `transformers` if one exists, which removes the remote-code requirement entirely.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 843 undertrained tokens (norm < 0.3× the vocabulary median of 7.801), including 176 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "DelaLika", "Kadaghanon", "Pagbuok", "Kapunoang", "Kasipak", "Kasarangang", "Pagklas", "Kaginharian". 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 behavioral 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 profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-22.
| architecture | deepseek_vl_v2 · 12 layers · 1280-dim |
| parameters | 3389.1M |
| vocabulary | 129,280 tokens |
| license | apache-2.0 |
| serialization | safetensors custom code |
| chat template | none |
| glitch-token surface | 843 undertrained candidates, 176 plain-ASCII |
Full measured fingerprint
| architectures | DeepseekOCR2ForCausalLM |
| library | transformers |
| pipeline | image-text-to-text |
| repo files | 16 |
| revision | aaa02f381194 |
| HF snapshot | 1.4M downloads · 1.1k likes · updated 2026-02-03 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 129,280×1280 |
| embedding norms | median 7.8007 · mean 7.6028 |
| lineage check | no claimed base model |
| glitch-token samples | "DelaLika", "Kadaghanon", "Pagbuok", "Kapunoang", "Kasipak", "Kasarangang", "Pagklas", "Kaginharian", "Beskjeftigelse", "Britonhon", "Kahutong", "kabungtor" |
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 07:43 | 16s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 129,280×1280 |
| glitch surface | 843 undertrained, 176 plain-ASCII |
| lineage check | not checked (no claimed base model) |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/deepseek-ai/DeepSeek-OCR-2)