Instructions to use taki555/Qwen3-0.6B-Art with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use taki555/Qwen3-0.6B-Art with Transformers:

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

pipe = pipeline("text-generation", model="taki555/Qwen3-0.6B-Art")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("taki555/Qwen3-0.6B-Art")
model = AutoModelForMultimodalLM.from_pretrained("taki555/Qwen3-0.6B-Art")
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 Settings

vLLM

How to use taki555/Qwen3-0.6B-Art with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "taki555/Qwen3-0.6B-Art"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taki555/Qwen3-0.6B-Art",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/taki555/Qwen3-0.6B-Art

SGLang

How to use taki555/Qwen3-0.6B-Art 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 "taki555/Qwen3-0.6B-Art" \
    --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": "taki555/Qwen3-0.6B-Art",
		"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 "taki555/Qwen3-0.6B-Art" \
        --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": "taki555/Qwen3-0.6B-Art",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use taki555/Qwen3-0.6B-Art with Docker Model Runner:
```
docker model run hf.co/taki555/Qwen3-0.6B-Art
```

Qwen3-0.6B-Art

This is the Chain-of-Thought (CoT) efficient version of the Qwen3-0.6B model, trained on the DeepScaleR-Easy dataset.

This model was introduced in the paper The Art of Efficient Reasoning: Data, Reward, and Optimization. Check the Project Page for more details.

Model Description

Large Language Models (LLMs) consistently benefit from scaled Chain-of-Thought (CoT) reasoning but also suffer from heavy computational overhead. Efficient reasoning aims to incentivize short yet accurate thinking trajectories, typically through reward shaping with Reinforcement Learning (RL).

This model follows a two-stage training paradigm: length adaptation and reasoning refinement. It is optimized to maintain a sufficient density of positive reward signals while avoiding the "short-is-correct" trap, demonstrating robust and generalized efficient reasoning capabilities.

Citation

@inproceedings{wu2026art,
  title={The Art of Efficient Reasoning: Data, Reward, and Optimization},
  author={Taiqiang Wu and Zenan Xu and Bo Zhou and Ngai Wong},
  year={2026},
  url={https://arxiv.org/pdf/2602.20945}
}