Instructions to use Simplified-Reasoning/SU-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Simplified-Reasoning/SU-01 with Transformers:

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

pipe = pipeline("text-generation", model="Simplified-Reasoning/SU-01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Simplified-Reasoning/SU-01")
model = AutoModelForCausalLM.from_pretrained("Simplified-Reasoning/SU-01")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Simplified-Reasoning/SU-01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Simplified-Reasoning/SU-01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Simplified-Reasoning/SU-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Simplified-Reasoning/SU-01

SGLang

How to use Simplified-Reasoning/SU-01 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 "Simplified-Reasoning/SU-01" \
    --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": "Simplified-Reasoning/SU-01",
		"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 "Simplified-Reasoning/SU-01" \
        --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": "Simplified-Reasoning/SU-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Simplified-Reasoning/SU-01 with Docker Model Runner:
```
docker model run hf.co/Simplified-Reasoning/SU-01
```

Add pipeline tag and library name to SU-01 metadata

by nielsr HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

nielsr

1 day ago

Hi there! I'm Niels from the community science team at Hugging Face.

This PR improves the model card for SU-01 by:

Adding the pipeline_tag: text-generation to the YAML metadata for better discoverability.
Adding library_name: transformers as the configuration files indicate compatibility with the Transformers library.
Ensuring the associated research paper is linked in the Markdown content.

The rest of the comprehensive documentation provided by the authors, including the performance benchmarks and training details, has been preserved.

Add pipeline tag and library name to SU-01 metadata9adcba89

rzzhan changed pull request status to merged about 23 hours ago

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