The dataset is derived by distilling DeepSeek-R1 using the data pipeline available on github. More info about the dataset can be found on the dataset card at OpenThoughts-114k dataset.

This model improves upon the Bespoke-Stratos-7B model, which used 17k examples (Bespoke-Stratos-17k dataset). The numbers reported in the table below are evaluated with our open-source tool Evalchemy.

	AIME24	MATH500	GPQA-Diamond	LCBv2 Easy	LCBv2 Medium	LCBv2 Hard	LCBv2 All
OpenThinker-7B	31.3	83.0	42.4	75.3	28.6	6.5	39.9
Bespoke-Stratos-7B	22.7	79.6	38.9	71.4	25.2	0.8	35.8
DeepSeek-R1-Distill-Qwen-7B	60	88.2	46.9	79.7	45.1	14.6	50.1
gpt-4o-0513	8.7	75.8	46.5	87.4	42.7	8.9	50.5
o1-mini	64	85.6	60	92.8	74.7	39.8	72.8

We are fully open-source. Our model weights, datasets, data generation code, evaluation code, and training code are all publicly available.

	Open Weights	Open Data	Open Code
OpenThinker-7B	✅	✅	✅
Bespoke-Stratos-7B	✅	✅	✅
DeepSeek-R1-Distill-Qwen-7B	✅	❌	❌
gpt-4o-0513	❌	❌	❌
o1-mini	❌	❌	❌

Intended uses & limitations

Apache 2.0 License

Training procedure

We used four 8xH100 nodes to train the model for 20 hours.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 32
gradient_accumulation_steps: 3
total_train_batch_size: 96
total_eval_batch_size: 256
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Framework versions

Transformers 4.46.1
Pytorch 2.3.0
Datasets 3.1.0
Tokenizers 0.20.3

More info can be found in our repository: https://github.com/open-thoughts/open-thoughts.

Citation

@misc{guha2025openthoughtsdatarecipesreasoning,
  title={OpenThoughts: Data Recipes for Reasoning Models}, 
  author={Etash Guha and Ryan Marten and Sedrick Keh and Negin Raoof and Georgios Smyrnis and Hritik Bansal and Marianna Nezhurina and Jean Mercat and Trung Vu and Zayne Sprague and Ashima Suvarna and Benjamin Feuer and Liangyu Chen and Zaid Khan and Eric Frankel and Sachin Grover and Caroline Choi and Niklas Muennighoff and Shiye Su and Wanjia Zhao and John Yang and Shreyas Pimpalgaonkar and Kartik Sharma and Charlie Cheng-Jie Ji and Yichuan Deng and Sarah Pratt and Vivek Ramanujan and Jon Saad-Falcon and Jeffrey Li and Achal Dave and Alon Albalak and Kushal Arora and Blake Wulfe and Chinmay Hegde and Greg Durrett and Sewoong Oh and Mohit Bansal and Saadia Gabriel and Aditya Grover and Kai-Wei Chang and Vaishaal Shankar and Aaron Gokaslan and Mike A. Merrill and Tatsunori Hashimoto and Yejin Choi and Jenia Jitsev and Reinhard Heckel and Maheswaran Sathiamoorthy and Alexandros G. Dimakis and Ludwig Schmidt},
  year={2025},
  eprint={2506.04178},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2506.04178}, 
}

shaffhausen/OpenThinker-7B

OpenThinker-7B

Intended uses & limitations

Training procedure

Training hyperparameters

Framework versions

Links

Citation

shaffhausen/
OpenThinker-7B