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 Settings

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
```

nielsr HF Staff commited on Feb 16, 2025

Commit

1910866

verified ·

1 Parent(s): 0dccf55

Improve model card: Add paper URL, pipeline tag and Github URL

Browse files

This PR improves the model card by adding the paper URL, the pipeline tag, and the Github URL. It also adds the blog post URLs in the description.

Files changed (1) hide show

README.md +9 -5

README.md CHANGED Viewed

@@ -1,14 +1,15 @@
 ---
-library_name: transformers
 datasets:
 - BAAI/TACO
 - tasksource/PRM800K
 language:
 - en
-base_model:
-- Qwen/Qwen2.5-32B-Instruct
-- NovaSky-AI/Sky-T1-32B-Preview
 license: apache-2.0
 ---
 ## Model Details
@@ -18,9 +19,11 @@ license: apache-2.0
 <!-- Provide a longer summary of what this model is. -->
 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](https://novasky-ai.github.io/posts/reduce-overthinking/) for more details.
 - **Developed by:** NovaSky Team from Sky Computing Lab at UC Berkeley.
 ## Training Details
@@ -71,3 +74,4 @@ Please considering citing our blog post if you found it useful for your research
   note         = {Accessed: 2025-01-23},
   year         = {2025}
 }

 ---
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+- NovaSky-AI/Sky-T1-32B-Preview
 datasets:
 - BAAI/TACO
 - tasksource/PRM800K
 language:
 - en
+library_name: transformers
 license: apache-2.0
+pipeline_tag: text-generation
 ---
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
 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](https://novasky-ai.github.io/posts/reduce-overthinking/) and [Sky-T1 blog post](https://novasky-ai.github.io/posts/sky-t1/) for more details.
 - **Developed by:** NovaSky Team from Sky Computing Lab at UC Berkeley.
+- **Paper:** [LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!](https://hf.co/papers/2502.07374)
+- **Code:** [https://github.com/NovaSky-AI/SkyThought](https://github.com/NovaSky-AI/SkyThought)
 ## Training Details
   note         = {Accessed: 2025-01-23},
   year         = {2025}
 }
+```