Comparison
open-r1 vs awesome-LLM-resources
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 awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a.
Markdown twin · open-r1 alternatives · awesome-LLM-resources alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | open-r1 | awesome-LLM-resources |
|---|---|---|
| Maintenance | Slowing (125d since push) As of 2w · github_public_v1 | Very active (2d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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 |
Tagline
- open-r1
- Fully open reproduction of DeepSeek-R1
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- open-r1
- 26k
- awesome-LLM-resources
- 8.8k
Forks
- open-r1
- 2.4k
- awesome-LLM-resources
- 950
Open issues
- open-r1
- 340
- awesome-LLM-resources
- 23
Language
- open-r1
- Python
- awesome-LLM-resources
- -
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.
- awesome-LLM-resources
- awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a
Persona
- open-r1
- -
- awesome-LLM-resources
- -
Runtime
- open-r1
- -
- awesome-LLM-resources
- -
License
- open-r1
- The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution.
- awesome-LLM-resources
- Apache-2.0
Last pushed
- open-r1
- Apr 2, 2026
- awesome-LLM-resources
- Aug 14, 2026
Categories
- open-r1
- Inference & Serving, Model Training
- awesome-LLM-resources
- AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- open-r1
- Slowing (36%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- open-r1
- 125d
- awesome-LLM-resources
- 2d
Open issues (now)
- open-r1
- 340
- awesome-LLM-resources
- 23
Stars delta
- open-r1
- Unknown
- awesome-LLM-resources
- +142 (30d)
Open issues delta
- open-r1
- Unknown
- awesome-LLM-resources
- -13 (30d)
Owner type
- open-r1
- Organization
- awesome-LLM-resources
- User
Full report
- open-r1
- Trust report
- awesome-LLM-resources
- Trust report
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.
Choose awesome-LLM-resources if…
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models.
- Also covers AI Agents, Developer Tools, Evaluation & Observability, LLM Frameworks.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
When NOT to use awesome-LLM-resources
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huggingface/open-r1) · observed Aug 6, 2026
- GitHub forks (huggingface/open-r1) · observed Aug 6, 2026
- Last push (huggingface/open-r1) · observed Apr 2, 2026
- License file (Apache-2.0) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Aug 14, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 10, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: open-r1 26k · awesome-LLM-resources 8.8k (synced Aug 6, 2026).
Common questions
- What is the difference between open-r1 and awesome-LLM-resources?
- open-r1: Fully open reproduction of DeepSeek-R1. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose open-r1 over awesome-LLM-resources?
- Choose open-r1 over awesome-LLM-resources 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 awesome-LLM-resources over open-r1?
- Choose awesome-LLM-resources over open-r1 when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Evaluation & Observability, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
- 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 awesome-LLM-resources?
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
- Is open-r1 or awesome-LLM-resources more popular on GitHub?
- open-r1 has more GitHub stars (26,423 vs 8,845). Stars measure visibility, not whether either tool fits your constraints.
- Are open-r1 and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to open-r1 or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at open-r1 alternatives and awesome-LLM-resources alternatives (open-r1 markdown twin, awesome-LLM-resources 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 awesome-LLM-resources?
- open-r1: Slowing. awesome-LLM-resources: 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 awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: open-r1 trust report; awesome-LLM-resources trust report.