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Valdemardi/
Sky-T1-32B-Preview-AWQ

Name: Sky-T1-32B-Preview-AWQ
Availability: InStock

NovaSky-AI/Sky-T1-32B-Preview

quantized

apache-2.0

autotrain_compatible

text-generation-inference

endpoints_compatible

4-bit

awq

Views

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Quantization Details

The quantized model was created using AutoAWQ version 3.2.7.post3 with quant_config:

{
  "zero_point": True,
  "q_group_size": 128,
  "w_bit": 4,
  "version": "GEMM"
}

Model Details

Model Description

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 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.

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
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 and AnyScale. We would like to thanks the academic feedback and support from the Still-2 Team, and Junyang Lin from the Qwen Team.

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}
}

Valdemardi/Sky-T1-32B-Preview-AWQ