Down to Earth: Cleaning Up DeepSeek-R1's Mess
Restoring DeepSeek-R1 Model for Exploring In-depth Insights into Data and Training Procedures by Hugging Face
DeepSeek, a Chinese tech firm, has recently unleashed DeepSeek-R1, a reasoning model that's been creating some buzz - but not without some questions lingering in the air. Hugging Face, a well-known player in the AI world, is stepping up to clear things up with their Open-R1 initiative.
DeepSeek-R1, a descendant of DeepSeek V3 and trained with pure reinforcement learning, boasts performance rivaling OpenAI's o1. However, slippery details like the datasets, training code, and scaling laws used for its creation still need clearing up. That's where Hugging Face comes into play, aiming to shake things up and bring transparency to the table.
On their official blog, Hugging Face admits that while the DeepSeek-R1 model weights are open, the datasets and code for its training are still not in the public domain. They see this as a missed opportunity for the entire research and industry community, and that's exactly what they aim to rectify with Open-R1.
The goal? To construct the missing pieces of DeepSeek-R1's puzzle, so everyone can create similar or even better models, using these blueprints and datasets.
Behind the Scenes: DeepSeek-R1's Secret Sauce (Enrichment)
DeepSeek-R1's multi-stage training pipeline begins with a careful selection of a small initial dataset to fine-tune the DeepSeek-V3-Base model. This is followed by reinforcement learning (RL) similar to DeepSeek-R1-Zero, where new data is created through rejection sampling on the RL checkpoint and combined with supervised data from DeepSeek-V3 across domains like writing, factual QA, and self-cognition. The result is a resilient model equivalent to other top-tier models like OpenAI-01-1217.
The training code for DeepSeek-R1 hasn't been explicitly detailed in public sources. Hugging Face's Open-R1 effort aims to change that, by reproducing the DeepSeek-R1 model and open-sourcing the missing pieces of the R1 pipeline to enable community involvement and development.
Scaling laws in DeepSeek-R1 involve refining the data and model capacity during a multi-stage training process, enhancing the model's reasoning capabilities.
Enhancing Collaboration: Hugging Face's Open-R1 Initiative (Enrichment)
Hugging Face's Open-R1 initiative sets out to demystify models like DeepSeek-R1 by making accessible datasets, open-source code, and knowledge-sharing platforms. This transparency aims to foster collaboration, innovation, and make advancements in AI reasoning capabilities a collaborative effort.
Open-R1 will provide datasets like the "open-r1/OpenThoughts-114k-math", which covers various reasoning tasks derived from DeepSeek-R1. This will encourage others to contribute by reproducing and expanding upon the DeepSeek-R1 pipeline, leading to further improvements in reasoning capabilities. The end goal? Empowering AI researchers and developers to take the reins and drive growth in AI reasoning technology.
- Hugging Face's Open-R1 initiative aims to open up the training code and datasets for DeepSeek-R1, which are currently not in the public domain, providing an opportunity for the research and industry community to collaborate.
- The Open-R1 effort by Hugging Face intends to reproduce the DeepSeek-R1 model and make the missing pieces of the R1 pipeline available for open-source use, enabling community involvement and development.
- DeepSeek-R1's training code and scaling laws, which have not been explicitly detailed in public sources, are areas that Hugging Face's Open-R1 initiative hopes to clarify, fostering transparency in the AI world.
- By making datasets like "open-r1/OpenThoughts-114k-math" available through the Open-R1 initiative, Hugging Face aims to encourage other AI researchers and developers to reproduce and expand upon the DeepSeek-R1 pipeline, contributing to advancements in AI reasoning capabilities.
