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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
- Slowing (125d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
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·1mo
- Employees
- 160·Wikidata (P1128 employees)·1mo
- Funding
- $235,000,000 (2023-08)·GraphCanon curated seed (public press)·1mo
- Commercial model
- OSS + managed cloud·GraphCanon curated seed·1mo
Install
pip install open-r1How it fits your stack(1)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
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 · Aug 6, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 6, 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 create R1-Zero. This will likely involve curating new, large-scale datasets for math, reasoning, and code.
- Step 3: show we can go from base model to RL-tuned via multi-stage training.
Installation
[!CAUTION] Libraries rely on CUDA 12.4. If you see errors related to segmentation faults, double check the version your system is running with
nvcc --version.
To run the code in this project, first, create a Python virtual environment using e.g. uv.
To install uv, follow the UV Installation Guide.
[!NOTE] As a shortcut, run
make installto setup development libraries (spelled out below). Afterwards, if everything is setup correctly you can try out the Open-R1 models.
uv venv openr1 --python 3.11 && source openr1/bin/activate && uv pip install --upgrade pip
[!TIP] For Hugging Face cluster users, add
export UV_LINK_MODE=copyto your.bashrcto suppress cache warnings fromuv
Next, install vLLM and FlashAttention:
uv pip install vllm==0.8.5.post1
uv pip install setuptools && uv pip install flash-attn --no-build-isolation
This will also install PyTorch v2.6.0 and it is very important to use this version since the vLLM binaries are compiled for it. You can then install the remaining dependencies for your specific use case via pip install -e .[LIST OF MODES]. For most contributors, we recommend:
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e ".[dev]"
Next, log into your Hugging Face and Weights and Biases accounts as follows:
huggingface-cli login
wandb login
Finally, check whether your system has Git LFS installed so that you can load and push models/datasets to the Hugging Face Hub:
git-lfs --version
If it isn't installed, run:
sudo apt-get install git-lfs
For agents
This page has a .md twin and JSON over the API.