GraphCanon updated Oct 10, 2026 · GitHub synced Oct 10, 2026
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Decision brief
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.
Good fit when
- Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes.
- Open-R1 is ideal if your system setup includes CUDA 12.4, as it can take advantage of specific compiled binaries for vLLM that require PyTorch `v2.6.0`.
Avoid when
- 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.
- 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.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (8d since push)
- As of Oct 10, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 10, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Hugging Face. Display-only - separate from trust and ranking.
- Company
- Hugging Face·GitHub org profile·Jul 11, 2026
- Employees
- 160·Wikidata (P1128 employees)·Jul 11, 2026
- Funding
- $235,000,000 (2023-08)·GraphCanon curated seed (public press)·Jul 11, 2026
- Commercial model
- OSS + managed cloud·GraphCanon curated seed·Jul 11, 2026
Install
pip install open-r1How it fits your stack(1)
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Evidence and technical details
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Overview
Open-source project aiming to replicate the DeepSeek-R1 models and its training pipelines. Involves model distillation, RL pipeline replication, and multi-stage training.
Capability facts
- Languages
- python
Source: github.language · Oct 10, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 10, 2026)
To run the code in this project, first, create a Python virtual environment using e.g. `uv`.Source link
Tags
README
Plan of attack We will use the DeepSeek R1 tech report as a guide, which can roughly be broken down into three main steps: Step 1: replicate the R1 Distill models by distilling a high quality corpus from DeepSeek R1. Step 2: replicate the pure RL pipeline that DeepSeek used to cr...
For agents
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