Instructions to use NovaSky-AI/Sky-T1-32B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NovaSky-AI/Sky-T1-32B-Preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NovaSky-AI/Sky-T1-32B-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NovaSky-AI/Sky-T1-32B-Preview")
model = AutoModelForCausalLM.from_pretrained("NovaSky-AI/Sky-T1-32B-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NovaSky-AI/Sky-T1-32B-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NovaSky-AI/Sky-T1-32B-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovaSky-AI/Sky-T1-32B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NovaSky-AI/Sky-T1-32B-Preview

SGLang

How to use NovaSky-AI/Sky-T1-32B-Preview with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NovaSky-AI/Sky-T1-32B-Preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovaSky-AI/Sky-T1-32B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NovaSky-AI/Sky-T1-32B-Preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovaSky-AI/Sky-T1-32B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NovaSky-AI/Sky-T1-32B-Preview with Docker Model Runner:
```
docker model run hf.co/NovaSky-AI/Sky-T1-32B-Preview
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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!

@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}
}