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
```

Add pipeline tag, link to paper, and cite the paper

by nielsr HF Staff - opened Feb 16, 2025

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

+15

-11

Files changed (1) hide show

README.md +15 -11

README.md CHANGED Viewed

@@ -1,14 +1,15 @@
 ---
-library_name: transformers
 datasets:
 - codeparrot/apps
 - BAAI/TACO
 - AI-MO/NuminaMath-CoT
 language:
 - en
-base_model:
-- Qwen/Qwen2.5-32B-Instruct
 license: apache-2.0
 ---
 ## Model Details
@@ -50,13 +51,16 @@ We use Llama-Factory for training. On 8 H100, the training takes 19 hours with D
 We would like to thanks the compute resources from [Lambda Lab](https://lambdalabs.com/service/gpu-cloud?srsltid=AfmBOop5FnmEFTkavVtdZDsLWvHWNg6peXtat-OXJ9MW5GMNsk756PE5) and [AnyScale](https://www.anyscale.com/). We would like to thanks the academic feedback and support from the [Still-2 Team](https://arxiv.org/pdf/2412.09413), and [Junyang Lin](https://justinlin610.github.io/) from the [Qwen Team](https://qwenlm.github.io/).
 ## 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}
-}

 ---
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
 datasets:
 - codeparrot/apps
 - BAAI/TACO
 - AI-MO/NuminaMath-CoT
 language:
 - en
+library_name: transformers
 license: apache-2.0
+pipeline_tag: text-generation
 ---
 ## Model Details
 We would like to thanks the compute resources from [Lambda Lab](https://lambdalabs.com/service/gpu-cloud?srsltid=AfmBOop5FnmEFTkavVtdZDsLWvHWNg6peXtat-OXJ9MW5GMNsk756PE5) and [AnyScale](https://www.anyscale.com/). We would like to thanks the academic feedback and support from the [Still-2 Team](https://arxiv.org/pdf/2412.09413), and [Junyang Lin](https://justinlin610.github.io/) from the [Qwen Team](https://qwenlm.github.io/).
 ## Citation
+Please considering citing our paper if you found it useful for your research. Thank you!
 ```bibtex
+@misc{zhu2025llms,
+  author       = {Wenxuan Zhu and Xiangru Tang and Ziyang Ma and Hongbo Zhang and Tianqi Chen},
+  title        = {LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!},
+  year         = {2025},
+  eprint={2502.07374},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url = {https://arxiv.org/abs/2502.07374}
+}
+```