Model page

deepseek-ai/DeepSeek-V2-Lite-Chat warn

downloads 925.4klikes 144license otherarch deepseek_v2params 15706.5Mupdated 2024-06-25

chat template: present · view on Hugging Face ↗

Scan coverage

Ingot runs three batteries against a model. What each one checks →

BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-22
Weights batteryWeights forensics — no GPU, no downloadcomplete 2026-08-22102,400-token embedding scanned · 2542 undertrained
Behavioral batteryLive-inference differentialsnot 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.

  1. Read every `.py` file in the repo before first load — this code runs in your process.
  2. 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.
  3. 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 2542 undertrained tokens (norm < 0.3× the vocabulary median of 6.085), including 112 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "IconSuccessEncoded", "IconErrorEncoded", "orangehilldev", "ExternalTaskPojo", "typeNameLink", "navBarCell", "textquoted", "Supamiu". 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.

  1. 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.
  2. 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.
  3. 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.

architecturedeepseek_v2 · 27 layers · 2048-dim
parameters15706.5M
vocabulary102,400 tokens
licenseother
serializationsafetensors custom code
chat templatepresent · sha256:8aeba567270fa9a8
glitch-token surface2,542 undertrained candidates, 112 plain-ASCII
Full measured fingerprint
architecturesDeepseekV2ForCausalLM
librarytransformers
pipelinetext-generation
repo files15
revision85864749cd61
HF snapshot925.4k downloads · 144 likes · updated 2024-06-25 · captured 2026-08-21
embedding tensormodel.embed_tokens.weight · BF16 · 102,400×2048
embedding normsmedian 6.0851 · mean 5.7857
lineage checkno claimed base model
glitch-token samples"IconSuccessEncoded", "IconErrorEncoded", "orangehilldev", "ExternalTaskPojo", "typeNameLink", "navBarCell", "textquoted", "Supamiu", "RecordedVote", "allClassesLink", "controlcap", "sympad"

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.

batterystatusqueueddurationattempts
weightscomplete2026-08-21 07:4323s1
weights run 2026-08-21 measurements
probes runglitch-norm-scan, lineage-norm-correlation
embedding tensormodel.embed_tokens.weight · BF16 · 102,400×2048
glitch surface2,542 undertrained, 112 plain-ASCII
lineage checknot checked (no claimed base model)

Verdict badge

Ship the verdict in your README — it always shows the latest published analysis:

Ingot verdict: warn

[![Ingot scan](https://ingot.tools/api/v1/models/deepseek-ai/DeepSeek-V2-Lite-Chat/badge.svg)](https://ingot.tools/models/deepseek-ai/DeepSeek-V2-Lite-Chat)
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