In a bold move to promote open knowledge and transparency in AI development, researchers at Hugging Face have launched the Open-R1 project, which seeks to replicate DeepSeek's R1 "reasoning" AI model from scratch and open-source all of its components, including the data used to train it.

The initiative comes just a week after DeepSeek released its R1 model, which sent markets into a frenzy due to its impressive performance on various benchmarks. While R1 is technically "open" under a permissive license, many of the tools used to build it remain shrouded in mystery, making it difficult for researchers to replicate and further develop the model.

"The R1 model is impressive, but there's no open data set, experiment details, or intermediate models available, which makes replication and further research difficult," said Elie Bakouch, one of the Hugging Face engineers on the Open-R1 project. "Fully open-sourcing R1's complete architecture isn't just about transparency – it's about unlocking its potential."

R1, a reasoning model, effectively fact-checks itself, which helps it avoid some of the pitfalls that normally trip up models. Reasoning models take a little longer – usually seconds to minutes longer – to arrive at solutions compared to a typical non-reasoning model. The upside is that they tend to be more reliable in domains such as physics, science, and math.

The Open-R1 project is less concerned about U.S. AI dominance than "fully opening the black box of model training," Bakouch told TechCrunch. He noted that, because R1 wasn't released with training code or training instructions, it's challenging to study the model in depth – much less steer its behavior.

"Having control over the data set and process is critical for deploying a model responsibly in sensitive areas," Bakouch said. "It also helps with understanding and addressing biases in the model. Researchers require more than fragments […] to push the boundaries of what's possible."

The goal of the Open-R1 project is to replicate R1 in a few weeks, relying in part on Hugging Face's Science Cluster, a dedicated research server with 768 Nvidia H100 GPUs. The Hugging Face engineers plan to tap the Science Cluster to generate data sets similar to those DeepSeek used to create R1.

To build a training pipeline, the team is soliciting help from the AI and broader tech communities on Hugging Face and GitHub, where the Open-R1 project is being hosted. "We need to make sure that we implement the algorithms and recipes correctly, but it's something a community effort is perfect at tackling, where you get as many eyes on the problem as possible," said Leandro von Werra, head of research at Hugging Face.

There's already significant interest in the Open-R1 project, with 10,000 stars on GitHub in just three days. If the project is successful, AI researchers will be able to build on top of the training pipeline and work on developing the next generation of open-source reasoning models, Bakouch said.

Bakouch hopes the Open-R1 project will not only yield a strong open-source replication of R1 but also a foundation for better models to come. "Rather than being a zero-sum game, open-source development immediately benefits everyone, including the frontier labs and the model providers, as they can all use the same innovations," he said.

While some AI experts have raised concerns about the potential for open-source AI abuse, Bakouch believes that the benefits outweigh the risks. "When the R1 recipe has been replicated, anyone who can rent some GPUs can build their own variant of R1 with their own data, further diffusing the technology everywhere," he said.

The Open-R1 project marks an important shift in the AI field, promoting openness and collaboration over secrecy and exclusivity. As Bakouch noted, "We're really excited about the recent open-source releases that are strengthening the role of openness in AI. It's an important shift for the field that changes the narrative that only a handful of labs are able to make progress, and that open-source is lagging behind."