deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct warn
Loading it runs custom code from the repo; glitch tokens that can silently corrupt ordinary input; Glitch tokens confirmed behaviorally (echo test).
chat template: present · view on Hugging Face ↗
Scan coverageStatic battery2026-08-27Weights battery2026-08-22Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-27 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-22102,400-token embedding scanned · 2525 undertrained |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-27 · 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 2525 undertrained tokens (norm < 0.3× the vocabulary median of 7.832), including 92 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "IconErrorEncoded", "IconSuccessEncoded", "orangehilldev", "ExternalTaskPojo", "textquoted", "linkedExternalProjectPath", "EDIPU", "cachedSer". 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.
medium Glitch tokens confirmed behaviorally (echo test)
Asked to repeat its own undertrained tokens verbatim, the model failed on 8/16 while repeating 8/8 matched normal tokens correctly — e.g. "IconErrorEncoded" → ""engal""; "IconSuccessEncoded" → ""br""; "orangehilldev" → ""enclent"". These strings, appearing in input as identifiers (usernames, SKUs, error codes), are rewritten silently. Greedy decoding, temperature 0, seed 0.
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
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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_v2 · 27 layers · 2048-dim |
| parameters | 15706.5M |
| vocabulary | 102,400 tokens |
| license | other |
| serialization | safetensors custom code |
| chat template | present · sha256:8aeba567270fa9a8 |
| glitch-token surface | 2,525 undertrained candidates, 92 plain-ASCII |
Full measured fingerprint
| architectures | DeepseekV2ForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 14 |
| revision | e434a23f91ba |
| HF snapshot | 610.8k downloads · 637 likes · updated 2024-07-03 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · BF16 · 102,400×2048 |
| embedding norms | median 7.8316 · mean 7.4632 |
| lineage check | no claimed base model |
| glitch-token samples | "IconErrorEncoded", "IconSuccessEncoded", "orangehilldev", "ExternalTaskPojo", "textquoted", "linkedExternalProjectPath", "EDIPU", "cachedSer", "RecordedVote", "lemanya", "controlcap", "sympad" |
Battery runs (3)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| gpu | complete | 2026-08-27 01:34 | 2m | 1 |
| gpu | complete | 2026-08-25 21:47 | 3m | 1 |
| weights | complete | 2026-08-21 07:43 | 20s | 1 |
gpu run 2026-08-27 — measurements
| probes run | glitch |
gpu run 2026-08-25 — measurements
| probes run | glitch |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · BF16 · 102,400×2048 |
| glitch surface | 2,525 undertrained, 92 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-Coder-V2-Lite-Instruct)