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Model: PAI/DistillQwen-ThoughtY-8B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+---
+# 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
+- [Models on Hugging Face](https://huggingface.co/alibaba-pai)
+- [OmniThought-0528 Dataset](https://huggingface.co/datasets/alibaba-pai/OmniThought-0528)
+- [EasyDistill Framework](https://github.com/modelscope/easydistill)
+
+
+## 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](https://arxiv.org/abs/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} 
+}
+```