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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.
recipes: Recipes - data curation steps and training strategies - for building our models Sky-T1-32B-Flash, Sky-T1-32B-Preview and Sky-T1-7B series.skythought/evals: Our data generation and evaluation library. We provide a convenient CLI for evaluation as well as a Scorer API for scoring during data curation and training (example).skythought/train: Training scripts for Sky-T1. We use Llama-Factory to perform training.skythought/skythought-rl: RL training code for Sky-T1-7B and Sky-T1-mini.You can install the latest release from PyPI or from source:
pip install skythought
# Clone the repository
git clone https://github.com/NovaSky-AI/SkyThought.git
cd SkyThought
# Create and activate a virtual environment (using uv here)
uv venv --python 3.10
source .venv/bin/activate
# Install the package in editable mode
uv pip install -e .
Running evaluation is as simple as:
skythought evaluate --model NovaSky-AI/Sky-T1-32B-Preview --task aime24
We support a wide variety of datasets in mathematics, science and coding:
For more details, please refer to our evaluation guide and the evaluation README.
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 | 86.4 | 81.4 | 92.2 | 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 | | OlympiadBench (Math, EN) | 59.79 | 46.74 | 62.17 | 59.2 |
We also evaluate on non-reasoning benchmarks (these are benchmarks for instruction-following, QA, etc) to test whether the model has traded-off capability in other domains for better performance in reasoning-related benchmarks.
| Metric | Sky-T1-32B-Preview | Qwen-2.5-32B-Instruct | QwQ-32B-Preview | Eval Implementation |
|---------|-------------------|---------------------|-----------------|-------------------|
| MMLU (0 shot; no CoT) | 78.36 | 74.14 | 71.23 | lm_eval |
| MMLU (5 shot; no CoT) | 82.46 | 82.62 | 82.32 | lm_eval |
| ARC-C (0 shot; no CoT) | 49.49 | 49.4 | 49.66 | lm_eval |
| IFEval | 75.79 | 78.74 | 42.51 | lm_eval |
| LLM-as-a-Judge | 9.12 | 9.19 | 8.30 | fastchat |
| MGSM (0 shot; direct) | 33 | 42.3 | 19.07 | lm_eval |
| MGSM (8-shot; direct) | 58.4 | 61.47 | 58.5 | lm_eval |
| BFCL-v3 | 53.18 | 58.92 | 17.41 | BFCL |
| Arena-Hard | 74.79 | 66.51 | 52.6 | Arena-Hard-Auto |
For more details, refer here.
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:
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, Anyscale, and Databricks. 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.
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