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Support · DeepSeek-R1-Distill

Will it last?

Ownership levelPartialnone·limited·partial·substantial·fullAnalytical input C ยท 61.6/100

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 8B, and confusing a distil with the full R1. Not exhaustive - the very large Llama-ecosystem community is the practical first stop.

Versions, changelog & cadence

The R1-Distill-Llama-8B/70B 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 the very large Llama-ecosystem community 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 use-restricted Llama licence (relative to the Qwen distils), the absence of own safety tuning, inherited China-aligned censorship, and quant-quality trade-offs at 8B. 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, 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 partially own these self-hosted Llama-base R1-Distill models: self-hosting keeps your data yours (data-control strong, reliability strong), but the Llama Community Licence's acceptable-use policy, 700M-MAU clause, and naming/attribution duties hold use-and-modify to moderate - so ownership is partial, one step below the Apache-2.0 Qwen distils (deepseek-r1-distill-qwen), which reach substantial. For friction-free ownership prefer the Qwen distils unless you specifically need the Llama toolchain; where you use these, honour the Llama terms, deploy behind your own guardrails, and note there is no EU open-source exemption on the licence axis.

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-Llama checkpoints are built on Meta Llama bases (Llama-8B on Llama 3.1, Llama-70B on Llama 3.3) and carry the Llama Community Licence - non-OSI, with an Acceptable Use Policy, the "700 million monthly active users" clause (a separate Meta licence required above it), and "Built with Llama" naming/attribution requirements.
Model cardread2026-08-03
R1-Distill-Llama model cards on the verified deepseek-ai HF org: dense 8B/70B models distilled from R1, 128K context, safetensors; widely mirrored/quantized through the Llama ecosystem, serving on consumer hardware via Ollama, llama.cpp, vLLM, SGLang.
Third-party analysisunverified2026-08-03
On public leaderboards the R1-Distill-Llama-70B is a notably strong reasoning model for its size; the 8B 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-Llama checkpoints are small (8B-70B), below the systemic-risk threshold; the Llama Community Licence is not FOSS, so no open-source exemption applies on the licence axis, and DeepSeek publishes no copyright policy or training-content summary.