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Support · DeepSeek-R1

Will it last?

Ownership levelSubstantialnone·limited·partial·substantial·fullAnalytical input C ยท 64.4/100

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 multi-node OOM / sharding for the 671B model, reasoning (think-tag) parsing at the client, and quant-engine version mismatches on lower-bit builds. This list is not exhaustive - the large community ecosystem is the practical first stop for deployment questions.

Versions, changelog & cadence

R1 and the R1-0528 refresh are published on the verified deepseek-ai organisation. There is no formal changelog document; the Hugging Face revision hash is the changelog anchor

  • pin it, and diff revisions when a refresh lands.

Security / vulnerability disclosure

No formal published vulnerability-disclosure or security policy was found for DeepSeek-R1; 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 are the primary channels for usage and deployment questions, backed by a large third-party community around the R1 weights and their distils.

Deprecation / end-of-life policy

No published deprecation or end-of-life policy for DeepSeek-R1 checkpoints. Older revisions remain downloadable, but there is no documented sunset commitment - recorded as a gap.

Tracked known issues

The standing issues are China-aligned censorship on sensitive topics, lighter safety coverage with no first-party guard model, and the operational burden of a 671B model. See Assess for the behavioural 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 and there is a detailed technical report, but the training data and code are closed, and the weights carry 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 distribution with checksums and a clear canonical source with no malicious-checkpoint incident (checklist ~5/8).
Governance3/5how accountable and well-documented the publisher isActive, named publisher (DeepSeek) with a verified org and a track record of technical reports, meeting the score-3 anchor.
What this means for adoptionYou substantially own self-hosted DeepSeek-R1: MIT permits commercial use, modification and distillation, and self-hosting keeps your data yours, so use-and-modify and data-control are both strong - ownership is substantial. It stops short of full because the training corpus and code are closed (not reproducible), the weights carry China-aligned filtering you cannot inspect, and there is no first-party guard model. Deploy behind your own guardrails, and treat EU high-stakes use as needing a self-assembled compliance package.

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, read: "This code repository and the model weights are licensed under the MIT License.
Model cardread2026-08-03
DeepSeek-R1 model card on the verified deepseek-ai HF org: 671B total / 37B active MoE, 128K context, safetensors; R1-0528 is a later revision "also subject to MIT License"; first-class serving (vLLM, SGLang, llama.cpp, Ollama) and an extensive community quant/distil ecosystem.
Third-party analysisunverified2026-08-03
On independent public leaderboards DeepSeek-R1 is among the strongest open-weight reasoning models (maths, coding, analysis); not OneHill-reproduced this session.
Third-party analysisunverified2026-08-03
Independent analysis notes DeepSeek open-weight models apply China-aligned content filtering on politically sensitive topics, with lighter safety tuning than Western frontier labs and no companion guard model.
Third-party analysisunverified2026-08-03
No public EU AI Act training-content summary, copyright policy, or GPAI documentation package is published for DeepSeek-R1, and the training corpus is not released.