Model
DeepSeek-R1-Distill
Publisher
DeepSeek (China)
Licence
Llama Community Licence (3.1 for 8B / 3.3 for 70B)
You 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.
Do you really own it?
Partial
none·limited·partial·substantial·full
Analytical input: AOI C · 61.6/100
The four ownership factors
Floor-weighted, not averaged. Nothing is weak, but use & modify is only moderate, so it misses the bar for substantial - strong on both use & modify and data control - and lands at partial.
1
Use and modify freelyCan you run, modify and adapt it with no gate and no field-of-use trap?
ModerateThe 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?
ModerateWeights 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.
3
ReliabilityIs it reliable and good enough for the job?
StrongPerformance 3, operational 5 and safety 3: solid reasoning-for-size with the broadest serving support of the distils; all three at or above 3, operational at 5, so reliability is strong. The caveat is the absence of own safety tuning / a first-party guard model.
How this scores (AOI sub-dimensions)
Operational5/5how practical it is to run, serve and maintain in productionEasy to run: small dense models on a Llama base, which carries the broadest toolchain and hardware support of any distil base - first-class serving (vLLM, SGLang, llama.cpp, Ollama) and extensive quantization on consumer hardware.
Safety3/5whether misuse risks are evaluated and guardrails are providedDocumented reasoning behaviour meeting the score-3 anchor, but these are research distils with no safety tuning of their own, inheriting R1's China-aligned topic censorship, and no first-party guard model ships - deployers must add their own guardrails.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
StrongSelf-hosted, the weights run entirely on your own infrastructure - including on-device at 8B - with no telemetry and no claw-back, so your data stays yours. The licence restrictions constrain use, not data residency.
How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
How the AOI score is computed
The seven dimensions above, each scored 0 to 5, weighted and summed to the 0 to 100 headline. The score is the analytical input behind the ownership verdict, not the verdict itself.
DimensionScoreWeightPoints
Openness3/50.1810.8
Provenance3/50.169.6
Legal2/50.166.4
Safety3/50.169.6
Performance3/50.148.4
Operational5/50.1212.0
Governance3/50.084.8
HeadlineC · 61.6/100
Sources
Every rating traces to a primary document. Read means the text was verified; unverified means it is known to exist but has not yet been read.
DocumentWhat it grounds
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.