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

Libraries

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

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

pipe = pipeline("text-generation", model="NovaSky-AI/Sky-T1-32B-Flash")
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-Flash")
model = AutoModelForCausalLM.from_pretrained("NovaSky-AI/Sky-T1-32B-Flash")
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-Flash 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-Flash"
# 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-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use NovaSky-AI/Sky-T1-32B-Flash 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-Flash" \
    --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-Flash",
		"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-Flash" \
        --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-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NovaSky-AI/Sky-T1-32B-Flash with Docker Model Runner:
```
docker model run hf.co/NovaSky-AI/Sky-T1-32B-Flash
```
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 preference optimized on top of Sky-T1-32B-Preview to significantly reduce generation lengths while maintaining accuracy. The performance is on par with o1-preview model in both math and coding, while reducing generation lengths by up to 57% relative to Sky-T1-32B-Preview. Please see our blog post for more details.

Developed by: NovaSky Team from Sky Computing Lab at UC Berkeley.

Training Details

Training Data

10K preference pairs in math and coding domains, generated by Sky-T1-32B-Preview.

Training Procedure

We perform Simple Policy Optimization (SimPO) with a batch size of 96, learning rate of 5e-7, gamma of 0.3, and beta of 2.0.

Speeds

We use Llama-Factory for training. On 8xH100, the SimPO training takes ~2.5 hours with DeepSpeed Zero-3 Offload.

Evaluation

		Sky-T1-32B-Preview	Sky-T1-32B-Flash	Qwen2.5-32B-Instruct	QwQ-32B- Base	DeepSeek-R1-Distill-Qwen-32B
Math500	Acc	88.6	88.6	76.2	89.2	90.8
	Avg Len	2124	1417 (-33%)	522	2089	2010
AIME24	Acc	43.3	43.3	16.7	50	66.7
	Avg Len	6881	4365 (-37%)	970	7379	9173
LCB Easy	Acc	87.4	89	84.6	90.7	91.2
	Avg Len	3415	2265 (-34%)	414	3255	2775
LCB Medium	Acc	56.8	56.3	40.8	56.3	76.7
	Avg Len	8263	4389 (-47%)	535	6742	6324
LCB Hard	Acc	17.9	17.9	9.8	17.1	38.2
	Avg Len	14564	6199 (-57%)	618	10450	10448
MMLU	Acc	82.4	81.7	80.1	85.2	82.1
	Avg Len	1087	799 (-17%)	312	1041	774
GPQA Diamond	Acc	56.8	56.6	45.5	52.5	62.6
	Avg Len	3503	2148 (-39%)	600	3302	5108

Acknowledgement

We would like to thanks the compute resources from Lambda Lab and AnyScale.

License

Apache-2.0

Citation

Please considering citing our blog post if you found it useful for your research. Thank you!

@misc{reduce_overthinking_2025,
  author       = {NovaSky Team},
  title        = {Think Less, Achieve More: Cut Reasoning Costs by 50% Without Sacrificing Accuracy},
  howpublished = {https://novasky-ai.github.io/posts/reduce-overthinking},
  note         = {Accessed: 2025-01-23},
  year         = {2025}
}