--- title: "Awesome-LLM-Compression vs open-r1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huangowen-awesome-llm-compression-vs-huggingface-open-r1" tools: ["huangowen-awesome-llm-compression", "huggingface-open-r1"] --- # Awesome-LLM-Compression vs open-r1 *GraphCanon updated Jul 12, 2026* ## Verdict Pick Awesome-LLM-Compression if awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases; 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. [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) reports 1.8k GitHub stars, 128 forks, and 0 open issues, last pushed Jun 30, 2026. [open-r1](https://github.com/huggingface/open-r1) has 26k stars, 2.4k forks, and 340 open issues, last pushed Apr 2, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Compression's repository](https://github.com/HuangOwen/Awesome-LLM-Compression) and [open-r1's repository](https://github.com/huggingface/open-r1). | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [open-r1](/tools/huggingface-open-r1.md) | | --- | --- | --- | | Tagline | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | Fully open reproduction of DeepSeek-R1 | | Stars | 1,848 | 26,401 | | Forks | 128 | 2,446 | | Open issues | 0 | 340 | | Language | - | Python | | Adopt for | Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT License | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | | Categories | LLM Frameworks, Inference & Serving | Model Training, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [open-r1](/tools/huggingface-open-r1.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 10d | 100d | | Open issues (now) | 0 | 340 | | Owner type | User | Organization | | Full report | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | [trust report](/tools/huggingface-open-r1/trust.md) | ## Decision facts: Awesome-LLM-Compression - **Requirements:** The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable. - **Adopt for:** Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. - **License detail:** MIT License ## 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. ## Choose when ### Choose Awesome-LLM-Compression if… - License: Awesome-LLM-Compression is MIT, open-r1 is Apache-2.0. - Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.. - Tags unique to Awesome-LLM-Compression: compression, research papers, training acceleration, efficiency. - Also covers LLM Frameworks. - When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### Choose open-r1 if… - License: open-r1 is Apache-2.0, Awesome-LLM-Compression is MIT. - 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: deepseek-r1, rl pipeline, vllm, python. - Also covers Model Training. - 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 Awesome-LLM-Compression - Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. - If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ## 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. ## Common questions ### What is the difference between Awesome-LLM-Compression and open-r1? Awesome-LLM-Compression: Awesome LLM compression research papers and tools to accelerate LLM training and inference.. open-r1: Fully open reproduction of DeepSeek-R1. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Compression over open-r1? Choose Awesome-LLM-Compression over open-r1 when License: Awesome-LLM-Compression is MIT, open-r1 is Apache-2.0; Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.; Tags unique to Awesome-LLM-Compression: compression, research papers, training acceleration, efficiency; Also covers LLM Frameworks; When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### When should I choose open-r1 over Awesome-LLM-Compression? Choose open-r1 over Awesome-LLM-Compression when License: open-r1 is Apache-2.0, Awesome-LLM-Compression is MIT; 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: deepseek-r1, rl pipeline, vllm, python; Also covers Model Training; 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 avoid Awesome-LLM-Compression? Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ### 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. ### Is Awesome-LLM-Compression or open-r1 more popular on GitHub? open-r1 has more GitHub stars (26,401 vs 1,848). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Compression and open-r1 open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Compression: MIT, open-r1: Apache-2.0). ### Where can I find alternatives to Awesome-LLM-Compression or open-r1? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Compression alternatives](/tools/huangowen-awesome-llm-compression/alternatives) and [open-r1 alternatives](/tools/huggingface-open-r1/alternatives) ([Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/alternatives.md), [open-r1 markdown twin](/tools/huggingface-open-r1/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/huangowen-awesome-llm-compression-vs-huggingface-open-r1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-Compression or open-r1? Awesome-LLM-Compression: Active. open-r1: 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 Awesome-LLM-Compression and open-r1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Compression trust report](/tools/huangowen-awesome-llm-compression/trust); [open-r1 trust report](/tools/huggingface-open-r1/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huangowen-awesome-llm-compression`](/api/graphcanon/graph?tool=huangowen-awesome-llm-compression) - 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/_