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Assess · DeepSeek-R1-Distill

Can you own it?

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

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 Meta Llama bases - Llama-8B (Llama 3.1) and Llama-70B (Llama 3.3) - are dense reasoning models distilled from DeepSeek-R1. They are intended for cost-sensitive reasoning and as fine-tuning bases within the Llama ecosystem. They are a separate entry from the Qwen-base distils because they inherit a different, more restrictive licence - the Llama Community Licence.

Out-of-scope: any unguarded customer-facing role (research distils with no safety tuning of their own); any use requiring topic-neutral factuality (inherited China-aligned filtering); and any use that breaches Meta's Acceptable Use Policy or the 700M-MAU clause.

Known limitations, bias & failure modes

The defining caveats are the use-restricted licence (relative to the Apache-2.0 Qwen distils) and the absence of own safety tuning. They inherit R1's China-aligned topic censorship, the distillation data is closed, and capability is strong-for-size rather than frontier-absolute (the 8B is modest). They emit explicit reasoning (think-tag) traces.

Openness tier & components

open_weights tier (dimension ceiling 3), with a conditional (Llama community) licence. Weights and documentation are open, but the distillation data is closed and training code partial. Meets the open-weights anchor; the restrictions bite on legal and ownership.

License terms & what you may do

These distils carry the Llama Community Licence (8B on Llama 3.1, 70B on Llama 3.3) - non-OSI, and materially more restrictive than the Apache-2.0 Qwen distils. Commercial use is allowed, but subject to Meta's Acceptable Use Policy, the "700 million monthly active users" clause (above which you must obtain a separate licence from Meta), and naming/attribution requirements ("Built with Llama"; derivative model names must include "Llama"). These flow down to derivatives. The field-of-use restrictions are why use-and-modify is moderate and ownership is partial - one step below the Qwen distils.

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). Widely mirrored and quantized via the Llama ecosystem (the broadest toolchain support of any distil base) - each mirror is a separate artifact whose trust equals its uploader, and redistribution must carry the Llama licence and AUP. Pin the revision, verify the checksum, prefer the canonical org.

EU AI Act posture

These are GPAI models but not systemic-risk (8B-70B). Unlike the Apache-2.0 Qwen distils, the Llama Community Licence is not FOSS (it carries acceptable-use and 700M-MAU restrictions), so the Article 53 open-source exemption does not apply on the licence axis. The systemic-risk duties do not arise, but the transparency obligations are not exemption-covered here, and DeepSeek publishes no copyright policy or training-content summary. You must also honour Meta's AUP and the 700M-MAU clause. Legal scores 2.

Benchmarks & evaluation

The 70B distil is a notable strong-for-size public result; the 8B 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?

Moderate

The Llama Community Licence permits commercial use and modification, but it is non-OSI with an Acceptable Use Policy, the 700M-MAU clause (a separate Meta licence above that threshold), and naming/attribution duties ('Built with Llama', derivative names must include 'Llama') that flow down to derivatives - so use-and-modify is moderate, not strong. Below the Apache-2.0 Qwen distils.

How this scores (AOI sub-dimensions)
Openness3/5how much is released - weights, data, code, licence - and how freelyOpen-weights tier: weights openly downloadable with open documentation, but the licence is conditional (Llama community use restrictions), the distillation data closed, training code partial.
Legal2/5how permissive and clean the licence is for real commercial useThe Llama Community Licence is non-OSI with acceptable-use and 700M-MAU field-of-use restrictions and naming/attribution duties, so the open-source exemption does not apply and DeepSeek publishes no copyright policy or training-content summary.
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.