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Assess · DeepSeek-R1-Distill

Can you own it?

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

This page is a projection of the one entry record, the Use & modify and Transparency factors that Assess 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

Intended & out-of-scope use

The DeepSeek-R1-Distill checkpoints built on Qwen2.5 bases (1.5B, 7B, 14B, 32B) are dense reasoning models distilled from DeepSeek-R1 into far smaller, single-GPU-friendly weights. They are intended for on-device / cost-sensitive reasoning and as cheap fine-tuning bases. They are a separate entry from the flagship R1 and from the Llama-base distils because they inherit a different licence - Apache-2.0, from their Qwen2.5 bases.

Out-of-scope: any unguarded customer-facing role (these are research distils with no safety tuning of their own); any use requiring topic-neutral factuality (they inherit R1's China-aligned filtering); and treating the 1.5B as a frontier model (it is modest).

Known limitations, bias & failure modes

The defining caveat is that these are research distils with no safety tuning of their own

  • deploy them behind your own safety system. They inherit R1's China-aligned topic censorship, the distillation data is closed, and capability is strong-for-size rather than frontier-absolute (the 1.5B especially). They emit explicit reasoning (think-tag) traces you must parse.

Openness tier & components

open_weights tier (dimension ceiling 3). Apache-2.0 weights and open documentation, but the distillation data is closed and training code partial. Meets the open-weights anchor.

License terms & what you may do

These distils carry Apache-2.0 - OSI-approved, permissive, with an explicit patent grant and no field-of-use restriction - inherited from the Qwen2.5 bases they build on. That is a cleaner licence than the Llama-base distils (deepseek-r1-distill-llama, Llama Community Licence) and the equal of the MIT family models on permissiveness. It is what makes use-and-modify strong and ownership substantial. Confirm the Apache-2.0 line per checkpoint card.

Supply-chain provenance

The canonical source is the verified deepseek-ai organisation on Hugging Face, safetensors with checksums, no malicious-checkpoint incident (checklist ~5/8). These are the most widely mirrored and quantized DeepSeek artifacts (they run on consumer hardware) - each mirror is a separate artifact whose trust equals its uploader. Pin the revision, verify the checksum, prefer the canonical org.

EU AI Act posture

These are GPAI models but not systemic-risk: at 1.5B-32B they are orders of magnitude below the 1e25-FLOP presumption. Apache-2.0 is a genuine FOSS licence, so the Article 53 open-source exemption applies to the transparency obligations - making these the most EU-comfortable DeepSeek artifacts. The obligations that survive the exemption for all GPAI (a copyright policy and a public training-content summary) are still not published by DeepSeek, so the position is good but not complete. Legal scores 3.

Benchmarks & evaluation

The 14B/32B distils are notable strong-for-size public results; the 1.5B is modest. OneHill has not re-run these benchmarks, so performance is a solid 3 (strong-for-size, not frontier-absolute) and no specific figures are asserted as verified.

How this scores

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

1

Use and modify freelyCan you run, modify and adapt it with no gate and no field-of-use trap?

Strong

Apache-2.0 (OSI, permissive, patent grant, ungated) inherited from the Qwen2.5 bases, with commercial use and any modification permitted and no field-of-use limit - a clean permissive grant, so use-and-modify is strong. A step above the Llama-base distils.

How this scores (AOI sub-dimensions)
Openness3/5how much is released - weights, data, code, licence - and how freelyOpen-weights tier: Apache-2.0 weights and open documentation, but the distillation data is closed, training code partial, evaluation partial.
Legal3/5how permissive and clean the licence is for real commercial useCleaner than the 671B family models: Apache-2.0 (OSI, permissive, patent grant) and, being small, not systemic-risk - so the open-source exemption applies to the transparency duties.
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.