--- title: "open-r1 vs oumi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-open-r1-vs-oumi-ai-oumi" tools: ["huggingface-open-r1", "oumi-ai-oumi"] --- # open-r1 vs oumi *GraphCanon updated Aug 23, 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 oumi if oumi is a tool for fine-tuning, evaluating, and deploying open-source large language models (LLMs) such as Gemma 4, Qwen3.5, Qwen3.6, gpt-oss, DeepSeek-R1, among others. [open-r1](https://github.com/huggingface/open-r1) reports 26k GitHub stars, 2.4k forks, and 340 open issues, last pushed Apr 2, 2026. [oumi](https://oumi.ai) has 9.4k stars, 784 forks, and 34 open issues, last pushed Aug 21, 2026. Figures are from public GitHub metadata via [open-r1's repository](https://github.com/huggingface/open-r1) and [oumi's repository](https://github.com/oumi-ai/oumi). | | [open-r1](/tools/huggingface-open-r1.md) | [oumi](/tools/oumi-ai-oumi.md) | | --- | --- | --- | | Tagline | Fully open reproduction of DeepSeek-R1 | Easily fine-tune, evaluate and deploy open source LLMs/VLMs | | Stars | 26,423 | 9,376 | | Forks | 2,447 | 784 | | Open issues | 340 | 34 | | Language | Python | 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. | Oumi is a tool for fine-tuning, evaluating, and deploying open-source large language models (LLMs) such as Gemma 4, Qwen3.5, Qwen3.6, gpt-oss, DeepSeek-R1, among others. | | Persona | - | - | | Runtime | - | - | | License | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | Oumi is released under Apache-2.0 license, providing users with a permissive free software license that includes the terms of the MIT License while also addressing patent liability issues. | | Categories | Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-r1](/tools/huggingface-open-r1.md) | [oumi](/tools/oumi-ai-oumi.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 125d | 1d | | Open issues (now) | 340 | 34 | | Stars delta | Unknown | +17 (30d) | | Open issues delta | Unknown | +3 (30d) | | Full report | [trust report](/tools/huggingface-open-r1/trust.md) | [trust report](/tools/oumi-ai-oumi/trust.md) | ## 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: oumi - **Requirements:** Requires Docker; Docker is used for standardized and portable environment deployments. - **Adopt for:** Oumi is a tool for fine-tuning, evaluating, and deploying open-source large language models (LLMs) such as Gemma 4, Qwen3.5, Qwen3.6, gpt-oss, DeepSeek-R1, among others. - **License detail:** Oumi is released under Apache-2.0 license, providing users with a permissive free software license that includes the terms of the MIT License while also addressing patent liability issues. ## 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 oumi if… - Requirements: Requires Docker; Docker is used for standardized and portable environment deployments.. - Tags unique to oumi: dpo, evaluation, fine-tuning, llms. - Also covers Evaluation & Observability. - oumi ships Docker support for self-hosted deployment. - - You are working specifically with one of the supported open-source LLMs including Gemma 4 or Qwen variants. ## 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 oumi - - If your focus is on proprietary models rather than open-source ones, Oumi may not offer the necessary support or integrations. - - You require deployment flexibility beyond what Oumi provides for less commonly supported open-source LLMs outside its primary focus (e.g., Gemma 4, Qwen series). ## Common questions ### What is the difference between open-r1 and oumi? open-r1: Fully open reproduction of DeepSeek-R1. oumi: Easily fine-tune, evaluate and deploy open source LLMs/VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose open-r1 over oumi? Choose open-r1 over oumi 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 oumi over open-r1? Choose oumi over open-r1 when Requirements: Requires Docker; Docker is used for standardized and portable environment deployments.; Tags unique to oumi: dpo, evaluation, fine-tuning, llms; Also covers Evaluation & Observability; oumi ships Docker support for self-hosted deployment; - You are working specifically with one of the supported open-source LLMs including Gemma 4 or Qwen variants. ### 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 oumi? - If your focus is on proprietary models rather than open-source ones, Oumi may not offer the necessary support or integrations. - You require deployment flexibility beyond what Oumi provides for less commonly supported open-source LLMs outside its primary focus (e.g., Gemma 4, Qwen series). ### Is open-r1 or oumi more popular on GitHub? open-r1 has more GitHub stars (26,423 vs 9,376). Stars measure visibility, not whether either tool fits your constraints. ### Are open-r1 and oumi open source? Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, oumi: Apache-2.0). ### Where can I find alternatives to open-r1 or oumi? GraphCanon lists graph-backed alternatives at [open-r1 alternatives](/tools/huggingface-open-r1/alternatives) and [oumi alternatives](/tools/oumi-ai-oumi/alternatives) ([open-r1 markdown twin](/tools/huggingface-open-r1/alternatives.md), [oumi markdown twin](/tools/oumi-ai-oumi/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-oumi-ai-oumi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, open-r1 or oumi? open-r1: Slowing. oumi: Very active. 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 oumi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-r1 trust report](/tools/huggingface-open-r1/trust); [oumi trust report](/tools/oumi-ai-oumi/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/_