--- title: "open-r1 vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-open-r1-vs-mlabonne-llm-course" tools: ["huggingface-open-r1", "mlabonne-llm-course"] --- # open-r1 vs llm-course *GraphCanon updated Oct 10, 2026* ## 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. [open-r1](https://github.com/huggingface/open-r1) reports 26k GitHub stars, 2.5k forks, and 0 open issues, last pushed Oct 2, 2026. [llm-course](https://mlabonne.github.io/blog/) has 83k stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [open-r1's repository](https://github.com/huggingface/open-r1) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [open-r1](/tools/huggingface-open-r1.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Fully open reproduction of DeepSeek-R1 | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 26,477 | 83,011 | | Forks | 2,452 | 9,657 | | Open issues | 0 | 90 | | Language | Python | - | | Adopt for | 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 provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. | | Persona | - | - | | Runtime | - | - | | License | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | Apache-2.0 | | Categories | Inference & Serving, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-r1](/tools/huggingface-open-r1.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 8d | 224d | | Open issues (now) | 0 | 90 | | Stars delta | +28 (30d) | +1.5k (30d) | | Open issues delta | -340 (30d) | +4 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/huggingface-open-r1/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [open-r1](/tools/huggingface-open-r1.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime ## Decision facts: open-r1 - **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. - **Adopt for:** 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. - **License detail:** The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. ## Decision facts: llm-course - **Adopt for:** llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/huggingface-open-r1/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([open-r1 markdown twin](/tools/huggingface-open-r1/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md)), 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](/compare/huggingface-open-r1-vs-mlabonne-llm-course.md) 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](/tools/huggingface-open-r1/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-open-r1`](/api/graphcanon/graph?tool=huggingface-open-r1) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_