| | ---
|
| | library_name: transformers
|
| | datasets:
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| | - codeparrot/apps
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| | - BAAI/TACO
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| | - AI-MO/NuminaMath-CoT
|
| | language:
|
| | - zho
|
| | - eng
|
| | - fra
|
| | - spa
|
| | - por
|
| | - deu
|
| | - ita
|
| | - rus
|
| | - jpn
|
| | - kor
|
| | - vie
|
| | - tha
|
| | - ara
|
| | base_model:
|
| | - Qwen/Qwen2.5-32B-Instruct
|
| | license: apache-2.0
|
| | ---
|
| |
|
| | ## Model Details
|
| |
|
| | ### Model Description
|
| |
|
| | <!-- Provide a longer summary of what this model is. -->
|
| |
|
| | This is a 32B reasoning model trained from Qwen2.5-32B-Instruct with 17K data. The performance is on par with o1-preview model on both math and coding.
|
| | Please see our [blog post](https://novasky-ai.github.io/posts/sky-t1/) for more details.
|
| |
|
| | - **Developed by:** NovaSky Team from Sky Computing Lab at UC Berkeley.
|
| |
|
| | ## Training Details
|
| |
|
| | ### Training Data
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| |
|
| | 17K verified correct responses from Qwen/QwQ-32B-Preview on coding, math. In addition, we add the science portion from the [Still-2 paper](https://arxiv.org/pdf/2412.09413).
|
| |
|
| | ### Training Procedure
|
| | We perform supervised fine tuning on the data, with a batch size of 96.
|
| |
|
| | #### Speeds
|
| |
|
| | We use Llama-Factory for training. On 8 H100, the training takes 19 hours with DeepSpeed Zero-3 Offload.
|
| |
|
| |
|
| | ## Evaluation
|
| | | | Sky-T1-32B-Preview | Qwen-2.5-32B-Instruct | QwQ | o1-preview |
|
| | |-----------------------|---------------------|--------|-------|------------|
|
| | | Math500 | 82.4 | 76.2 | 85.4 | 81.4 |
|
| | | AIME2024 | 43.3 | 16.7 | 50.0 | 40.0 |
|
| | | LiveCodeBench-Easy | 86.3 | 84.6 | 90.7 | 92.9 |
|
| | | LiveCodeBench-Medium | 56.8 | 40.8 | 56.3 | 54.9 |
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| | | LiveCodeBench-Hard | 17.9 | 9.8 | 17.1 | 16.3 |
|
| | | GPQA-Diamond | 56.8 | 45.5 | 52.5 | 75.2 |
|
| |
|
| | ## Acknowledgement
|
| | We would like to thanks the compute resources from [Lambda Lab](https://lambdalabs.com/service/gpu-cloud?srsltid=AfmBOop5FnmEFTkavVtdZDsLWvHWNg6peXtat-OXJ9MW5GMNsk756PE5) and [AnyScale](https://www.anyscale.com/). We would like to thanks the academic feedback and support from the [Still-2 Team](https://arxiv.org/pdf/2412.09413), and [Junyang Lin](https://justinlin610.github.io/) from the [Qwen Team](https://qwenlm.github.io/).
|
| |
|
| | ## Citation
|
| | Please considering citing our blog post if you found it useful for your research. Thank you!
|
| |
|
| | ```bibtex
|
| | @misc{sky_t1_2025,
|
| | author = {NovaSky Team},
|
| | title = {Sky-T1: Fully open-source reasoning model with o1-preview performance in $450 budget},
|
| | howpublished = {https://novasky-ai.github.io/posts/sky-t1},
|
| | note = {Accessed: 2025-01-09},
|
| | year = {2025}
|
| | } |
