deepseek-ai/DeepSeek-V3 warn
Loading it runs custom code from the repo; no license declared — no usage rights by default; glitch tokens that can silently corrupt ordinary input.
chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-22Weights battery2026-08-22Behavioral batterynot rundetails
| 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 · 607 undertrained |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
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 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 607 undertrained tokens (norm < 0.3× the vocabulary median of 3.196), including 59 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "kabungtor", "unisipyo", "ultatua", "pagklas", "jeftigelse", "bingkil", "nahimut", "asarangang". 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.
Check every checkpoint before it ships
Use the web app, API, CLI, or CI gate to scan candidate checkpoints and catch model drift before deployment. Public-model scans publish to the open database; paid plans add the volume needed for continuous checks.
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_v3 · 61 layers · 7168-dim |
| parameters | 684531.4M |
| vocabulary | 129,280 tokens |
| license | none declared |
| serialization | safetensors custom code |
| chat template | present · sha256:3b8267e54b67df65 |
| glitch-token surface | 607 undertrained candidates, 59 plain-ASCII |
Full measured fingerprint
| architectures | DeepseekV3ForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 185 |
| revision | e815299b0bcb |
| HF snapshot | 1.0M downloads · 4.2k likes · updated 2025-03-27 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 129,280×7168 |
| embedding norms | median 3.196 · mean 3.1572 |
| lineage check | no claimed base model |
| glitch-token samples | "kabungtor", "unisipyo", "ultatua", "pagklas", "jeftigelse", "bingkil", "nahimut", "asarangang", "Pagklas", "ugnawan", "Kadaghan", "unoang" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 07:43 | 2m | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 129,280×7168 |
| glitch surface | 607 undertrained, 59 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-V3)