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+# DistilQwen-100k/DistilQwen-1M: High-Quality Instruction-Tuning Datasets  
+
+## Overview  
+To empower community developers in enhancing the **instruction-following capabilities** of large language models (LLMs), we open-source **`DistilQwen-100k`** and **`DistilQwen-1M`**, subsets of the training data used for the **DistilQwen model series**. The datasets provide diverse, high-quality samples to improve model performance in key areas.  
+
+## Dataset Features  
+- **Scale**: **100 thousand**/**1 million** meticulously distilled entries.  
+- **Coverage**: Balanced mix of:  
+  - **Mathematics**  
+  - **Code generation & understanding**  
+  - **Knowledge-based QA**  
+  - **Instruction following**  
+  - **Creative generation**  
+- **Purpose**: Optimized for **instruction tuning**, helping models retain generalization while adapting to downstream tasks.  
+
+## Use Cases  
+- **Fine-tuning LLMs**: Mitigate *catastrophic forgetting* by combining with custom datasets.  
+- **Multi-task learning**: Improve coherence in mathematical reasoning, coding, and creative tasks.  
+- **Research**: Study distillation techniques or instruction-tuning efficacy.  
+
+## Use the Datasets
+```python
+from datasets import load_dataset
+
+# Login using e.g. `huggingface-cli login` to access this dataset
+ds = load_dataset("alibaba-pai/DistilQwen_100k")
+ds = load_dataset("alibaba-pai/DistilQwen_1M")
+```
+
+## Reference
+
+For more detailed information about the dataset construction process, we encourage you to refer to our paper:
+
+- **DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models**  
+  Chengyu Wang, Junbing Yan, Yuanhao Yue, Jun Huang  
+  [arXiv:2504.15027](https://arxiv.org/abs/2504.15027)
+
+You can cite the paper using the following citation format:
+
+```bibtex
+@misc{wang2025distilqwen25industrialpracticestraining,
+      title={DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models}, 
+      author={Chengyu Wang and Junbing Yan and Yuanhao Yue and Jun Huang},
+      year={2025},
+      eprint={2504.15027},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2504.15027}
+}
+```