Alpha
Ctrl K
Contact
Support · DeepSeek-R1-Distill

Will it last?

Ownership levelSubstantialnone·limited·partial·substantial·fullAnalytical input C ยท 64.8/100

This page is a projection of the one entry record, the Transparency factor that Support covers. The full verdict is set by all four factors together, floor-weighted so the weakest caps the whole.

Which domain expands which factor
  • AssessUse & modify + Transparency
  • ImplementData control + Reliability
  • UseReliability
  • SupportTransparency

Common problems & fixes

The recurring issues are reasoning / think-tag parsing at the client, quant-quality trade-offs at the smallest sizes, and confusing a distil with the full R1. Not exhaustive - the very large community around these consumer-runnable models is the practical first stop.

Versions, changelog & cadence

The R1-Distill-Qwen sizes are published on the verified deepseek-ai organisation. There is no formal changelog document; the Hugging Face revision hash is the changelog anchor.

Security / vulnerability disclosure

No formal published vulnerability-disclosure or security policy was found; the only contact is the general deepseek-ai org presence. Recorded as a gap - factor it into your own incident-response plan.

Community & support channels

The deepseek-ai Hugging Face organisation and GitHub, plus a very large community ecosystem around these consumer-runnable distils.

Deprecation / end-of-life policy

No published deprecation or end-of-life policy for the distil checkpoints. Older revisions remain downloadable, but there is no documented sunset commitment - recorded as a gap.

Tracked known issues

The standing issues are the absence of own safety tuning, inherited China-aligned censorship, and quant-quality trade-offs at the smallest sizes. See Assess for the detail.

How this scores

The ownership factor this domain covers, drawn from the one entry record.

2

TransparencyDo you know what it is: weights, training, behaviour, and legible terms?

Moderate

Weights are inspectable under Apache-2.0, but the distillation data and code are closed and the weights inherit R1's China-aligned topic filtering you cannot inspect - open_weights, so moderate.

How this scores (AOI sub-dimensions)
Provenance3/5how well we can trace and verify what went into the modelVerified deepseek-ai org on Hugging Face, safetensors with checksums, clear canonical source, no malicious-checkpoint incident (checklist ~5/8).
Governance3/5how accountable and well-documented the publisher isActive, named publisher (DeepSeek) with a verified org and technical reports, meeting the score-3 anchor.
What this means for adoptionYou substantially own these self-hosted Apache-2.0 R1-Distill (Qwen) models: a clean permissive licence makes use-and-modify strong, and self-hosting - including on-device at the small sizes - keeps your data yours, so data-control is strong too. Ownership is substantial, a step above the Llama-base distils. It stops short of full because the distillation data and code are closed (not reproducible) and the weights inherit R1's China-aligned filtering and carry no safety tuning of their own - deploy behind your own guardrails.

Sources

The same evidence records as the entry sheet. Read means the text was verified; unverified means it is known to exist but not yet read.

Licenceread2026-08-03
DeepSeek-R1 README + the distil checkpoint cards, read: the R1-Distill-Qwen checkpoints are built on Qwen2.5 bases (1.5B/7B/14B/32B) and "are derived from ...
Model cardread2026-08-03
R1-Distill-Qwen model cards on the verified deepseek-ai HF org: dense 1.5B/7B/14B/32B models distilled from R1, 128K context (from the Qwen2.5 bases), safetensors; the most widely mirrored/quantized DeepSeek artifacts, serving on consumer hardware via Ollama, llama.cpp, vLLM, SGLang.
Third-party analysisunverified2026-08-03
On public leaderboards the R1-Distill-Qwen-14B/32B are notably strong reasoning models for their size; the 1.5B is modest.
Third-party analysisunverified2026-08-03
The R1-Distill checkpoints are research distils with no safety tuning of their own and inherit R1's China-aligned content filtering; no companion guard model ships.
Third-party analysisunverified2026-08-03
The R1-Distill-Qwen checkpoints are small (1.5B-32B), well below the systemic-risk threshold; their Apache-2.0 licence is FOSS, so the open-source exemption applies to the transparency duties, but DeepSeek publishes no copyright policy or training-content summary.