---
library_name: transformers
datasets:
- codeparrot/apps
- BAAI/TACO
- AI-MO/NuminaMath-CoT
language:
- en
base_model:
- Qwen/Qwen2.5-32B-Instruct
---

## 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

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
| Model                  | Math500 | AIME2024 | LiveCodeBench-Easy | LiveCodeBench-Medium | LiveCodeBench-Hard | GPQA-Diamond |
|------------------------|---------|----------|---------------------|----------------------|--------------------|--------------|
| Qwen-2.5-3 2B-Instruct | 85.2    | 16.7     | 82.4                | 40.0                 | 8.9                | 42.9         |
| Sky-T1                 | 88.6    | 43.3     | 87.9                | 54.4                 | 17.1               | 53.5         |
| QwQ                    | 90.6    | 50.0     | 88.7                | 57.3                 | 17.9               | 56.6         |
| o1-preview             | 85.5    | 46.6     | 92.0                | 56.6                 | 13.8               | 73.3         |

## 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}
}