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alibaba-pai/
DistilQwen-ThoughtY-4B

Name: DistilQwen-ThoughtY-4B
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DistillQwen-ThoughtY: Enhanced Chain-of-Thought Reasoning Models

Key Contributions

Advanced Reasoning Models: DistillQwen-ThoughtY series (4B/8B/32B) outperform previous versions (ThoughtX) and Qwen3 in thinking mode, achieving state-of-the-art results on mathematical, scientific, and coding tasks.
OmniThought-0528 Dataset: New 365K high-quality Chain-of-Thought (CoT) dataset distilled from DeepSeek-R1-0528 (top-tier Chinese model) with:
- Cognitive Difficulty (CD) and Reasoning Verbosity (RV) annotations
- Multi-teacher integration (DeepSeek-R1, DeepSeek-R1-0528, QwQ-32B)

Performance Highlights

Model	AIME2024	MATH500	GPQA Diamond	LiveCodeBench V2	Avg.
DistillQwen-ThoughtY-4B	76.7	95.2	56.1	75.8	76.0
DistillQwen-ThoughtY-8B	76.7	94.6	62.1	78.1	77.9
DistillQwen-ThoughtY-32B	90.0	95.2	63.6	76.3	81.3
OpenThinker2-32B	76.7	90.8	64.1	72.5	76.0
DistillQwen-ThoughtX-32B	80.0	92.6	64.0	73.4	77.5

Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "alibaba-pai/DistillQwen-ThoughtY-4B",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
inputs = tokenizer([text], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=32768)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Resources

Reference

For more detailed information about the model, we encourage you to refer to our paper:

Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
Wenrui Cai, Chengyu Wang, Junbing Yan, Jun Huang, Xiangzhong Fang arXiv:2505.10937

You can cite the paper using the following citation format:

bibtex

@misc{cai2025reasoningomnithoughtlargecot,
      title={Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations}, 
      author={Wenrui Cai and Chengyu Wang and Junbing Yan and Jun Huang and Xiangzhong Fang},
      year={2025},
      eprint={2505.10937},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.10937} 
}

alibaba-pai/DistilQwen-ThoughtY-4B