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Comparison

open-r1 vs llm-course

Verdict

Pick open-r1 if open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training; pick llm-course if llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks.

Markdown twin · open-r1 alternatives · llm-course alternatives

GraphCanon updated Oct 10, 2026

15views this month

open-r1 logo

open-r1

huggingface/open-r1

★ 26kpushed Oct 2, 2026
vs
llm-course logo

llm-course

mlabonne/llm-course

★ 83kpushed Feb 5, 2026

Trust & integrity

Signalopen-r1llm-course
Maintenance
Active (8d since push)
As of Oct 10, 2026 · github_public_v1
Slowing (224d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Oct 10, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
open-r1 trust reportllm-course trust reportTrust methodology

Tagline

open-r1
Fully open reproduction of DeepSeek-R1
llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Stars

open-r1
26k
llm-course
83k

Forks

open-r1
2.5k
llm-course
9.7k

Open issues

open-r1
0
llm-course
90

Language

open-r1
Python
llm-course
-

Adopt for

open-r1
Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training.
llm-course
llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks.

Persona

open-r1
-
llm-course
-

Runtime

open-r1
-
llm-course
-

License

open-r1
The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
llm-course
Apache-2.0

Last pushed

open-r1
Oct 2, 2026
llm-course
Feb 5, 2026

Categories

open-r1
Inference & Serving, Model Training
llm-course
Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training

Trust and health

Maintenance

open-r1
Active (82%)
llm-course
Slowing (36%)

Days since push

open-r1
8d
llm-course
224d

Open issues (now)

open-r1
0
llm-course
90

Stars delta

open-r1
+28 (30d)
llm-course
+1.5k (30d)

Open issues delta

open-r1
-340 (30d)
llm-course
+4 (30d)

Owner type

open-r1
Organization
llm-course
User

Full report

open-r1
Trust report
llm-course
Trust report

Shared compatibility

  • Python · open-r1: Python runtime · llm-course: Python runtime

Choose open-r1 if…

  • Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical..
  • Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation.
  • Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.

When NOT to use open-r1

  • Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors.
  • Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming.
GitHubDetails

Choose llm-course if…

  • Tags unique to llm-course: course, large-language-models, llm, machine-learning.
  • Also covers Developer Tools, Evaluation & Observability, LLM Frameworks.
  • Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.

When NOT to use llm-course

  • Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation.
  • Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum.
  • Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.
GitHubDetails

Explore

open-r1 trust report →llm-course trust report →Inference & Serving category →Model Training category →Developer Tools category →Evaluation & Observability category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: open-r1 26k · llm-course 83k (synced Oct 10, 2026).

Common questions

What is the difference between open-r1 and llm-course?
open-r1: Fully open reproduction of DeepSeek-R1. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories.
When should I choose open-r1 over llm-course?
Choose open-r1 over llm-course when Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.
When should I choose llm-course over open-r1?
Choose llm-course over open-r1 when Tags unique to llm-course: course, large-language-models, llm, machine-learning; Also covers Developer Tools, Evaluation & Observability, LLM Frameworks; Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.
When should I avoid open-r1?
Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch v2.6.0, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming.
When should I avoid llm-course?
Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.
Is open-r1 or llm-course more popular on GitHub?
llm-course has more GitHub stars (83,011 vs 26,477). Stars measure visibility, not whether either tool fits your constraints.
Are open-r1 and llm-course open source?
Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, llm-course: Apache-2.0).
Where can I find alternatives to open-r1 or llm-course?
GraphCanon lists graph-backed alternatives at open-r1 alternatives and llm-course alternatives (open-r1 markdown twin, llm-course markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, open-r1 or llm-course?
open-r1: Active. llm-course: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for open-r1 and llm-course?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; llm-course trust report.

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