- [2025/01/10] 🎉 We have released our Sky-T1-32B-Preview model and data through HuggingFace!
We open source the code and scripts we used for data curation, training, and evaluation for Sky-T1-32B-Preview, you can find more details in each directory.
/data: The 17k training data used to train Sky-T1-32B-Preview. We also add the science and riddle portion from the STILL-2 model.skythought/tools: Training data curation and evaluation for Sky-T1. To generate our training data, we use the QwQ-32B-Preview model. We curate the data mixture to cover diverse domains that require reasoning, and a reject sampling procedure to improve the data quality.skythought/train: Training scripts for Sky-T1. We use Llama-Factory to perform training. The model was trained for 3 epochs with a learning rate of 1e-5 and a batch size of 96. Our model training was completed in 19 hours on 8 H100 GPUs using DeepSpeed Zero-3 offloading, costing approximately $450 as per Lambda Cloud pricing.
Following, we show our evaluation results for the Sky-T1-32B-Preview model across math, coding, and science benchmarks.
| Metric | 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 |
| LiveCodeBench-Hard | 17.9 | 9.8 | 17.1 | 16.3 |
| GPQA-Diamond | 56.8 | 45.5 | 52.5 | 75.2 |
We believe that open-source collaboration drives progress, and with Sky-T1-32B-Preview, we are fully committed to empowering the community. We open-source all details (i.e., data, codes, model weights) to enable the community to replicate and improve on our results easily:
| Model | Sky-T1-32B-Preview |
STILL-2 |
Journey |
QwQ |
o1 |
|---|---|---|---|---|---|
| Data | ✅ |
✅ |
❌ |
❌ |
❌ |
| Code | ✅ |
❌ |
❌ |
❌ |
❌ |
| Report | ✅ |
✅ |
✅ |
❌ |
❌ |
| Math domain | ✅ |
✅ |
✅ |
✅ |
✅ |
| Coding domain | ✅ |
❌ |
❌ |
✅ |
✅ |
| Model Weights | ✅ |
✅ |
❌ |
✅ |
❌ |
The code in this repository is mostly described in the post below. Please consider citing this work if you find the repository helpful.
@misc{sky_t1_2025,
author = {NovaSky Team},
title = {Sky-T1: Train your own O1 preview model within $450},
howpublished = {https://novasky-ai.github.io/posts/sky-t1},
note = {Accessed: 2025-01-09},
year = {2025}
}This work is done at Berkeley Sky Computing Lab, with the amazing compute support from Lambda Labs and Anyscale. We would like to express our gratitude for the valuable academic feedback and support from the Still-2 Team, and Junyang Lin from the Qwen Team.
