OpenThinker2-7B
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the OpenThoughts2-1M dataset.
The OpenThinker2-7B model is the top 7B open-data reasoning model. It delivers performance comparable to state of the art 7B models like DeepSeek-R1-Distill-7B across a suite of tasks. This model improves upon our previous OpenThinker-7B model, which was trained on 114k examples from OpenThoughts-114k. The numbers reported in the table below are evaluated with our open-source tool Evalchemy.
| Model | Data | AIME24 | AIME25 | AMC23 | MATH500 | GPQA-D | LCBv2 |
|---|---|---|---|---|---|---|---|
| OpenThinker2-7B | ✅ | 50.0 | 33.3 | 89.5 | 88.4 | 49.3 | 55.6 |
| OpenThinker-7B | ✅ | 31.3 | 23.3 | 74.5 | 83.2 | 42.9 | 38.0 |
| DeepSeek-R1-Distill-Qwen-7B | ❌ | 57.3 | 33.3 | 92.0 | 89.6 | 47.3 | 48.4 |
| OlympicCoder-7B | ✅ | 20.7 | 15.3 | 63.0 | 74.8 | 25.3 | 55.4 |
| OpenR1-Qwen-7B | ✅ | 48.7 | 34.7 | 88.5 | 87.8 | 21.2 | 9.5 |
Data
This model was trained on the OpenThoughts2-1M dataset.
The OpenThoughts2-1M dataset was constructed by augmenting OpenThoughts-114k with existing datasets like OpenR1, as well as additional math and code reasoning data. We generate the additional math and code data by ablating over 26 different question generation methodologies and sampling from the highest performing ones.
See the OpenThoughts2-1M dataset page or our blog post for additional information.
Intended uses & limitations
Apache 2.0 License
Training procedure
We used 32 8xA100 nodes to train the model for 36 hours.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- seed: 42
- distributed_type: multi-GPU
- num_devices: 256
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- 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: 5.0
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{openthoughts,
author = {Team, OpenThoughts},
month = jan,
title = {{Open Thoughts}},
howpublished = {https://open-thoughts.ai},
year = {2025}
}
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