| --- |
| base_model: |
| - Qwen/Qwen3-4B |
|
|
| tags: |
| - distillation |
| - distilled |
| - sft |
| - peft |
| - qwen3 |
|
|
| datasets: |
| - ianncity/KIMI-K2.5-550000x |
| - Jackrong/Qwen3.5-reasoning-700x |
| - nohurry/Opus-4.6-Reasoning-3000x-filtered |
| - TeichAI/claude-4.5-opus-high-reasoning-250x |
| - TeichAI/gemini-3-pro-preview-high-reasoning-250x |
| - TeichAI/claude-haiku-4.5-high-reasoning-1700x |
| - TeichAI/gpt-5.2-high-reasoning-250x |
| - Roman1111111/gemini-3.1-pro-hard-high-reasoning |
| - Jackrong/glm-4.7-multiturn-CoT |
| - bmeyer2025/glm5-reasoning-traces |
| - TeichAI/claude-sonnet-4.5-high-reasoning-250x |
| - TeichAI/deepseek-v3.2-speciale-openr1-math-3k |
| - TeichAI/deepseek-v3.2-speciale-OpenCodeReasoning-3k |
| - TeichAI/deepseek-v3.2-speciale-1000x |
| - TeichAI/gpt-5-codex-1000x |
|
|
| model-index: |
| - name: hadadxyz/Qwen3-4B-Diversity |
| results: |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 67.8 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Humanities |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 57.9 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Formal Logic |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 58.7 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School European History |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 78.2 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Us History |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 84.8 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School World History |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 83.1 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu International Law |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 77.7 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Jurisprudence |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 78.7 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Logical Fallacies |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 82.8 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Moral Disputes |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 71.1 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Moral Scenarios |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 28.4 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Philosophy |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 73.3 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Prehistory |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 76.2 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Professional Law |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 47.4 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu World Religions |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 78.4 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Other |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 72.1 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Business Ethics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 73.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Clinical Knowledge |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 75.5 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu College Medicine |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 71.1 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Global Facts |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 41.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Human Aging |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 67.7 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Management |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 84.5 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Marketing |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 85.5 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Medical Genetics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 75.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Miscellaneous |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 79.7 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Nutrition |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 74.8 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Professional Accounting |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 55.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Professional Medicine |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 71.7 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Virology |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 53.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Social Sciences |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 78.4 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Econometrics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 64.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Geography |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 84.3 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Government And Politics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 87.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Macroeconomics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 74.6 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Microeconomics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 80.7 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Psychology |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 87.2 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Human Sexuality |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 75.6 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Professional Psychology |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 71.2 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Public Relations |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 71.8 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Security Studies |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 74.3 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Sociology |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 84.1 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Us Foreign Policy |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 81.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Stem |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 68.1 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Abstract Algebra |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 45.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Anatomy |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 61.5 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Astronomy |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 78.9 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu College Biology |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 83.3 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu College Chemistry |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 54.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu College Computer Science |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 69.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu College Mathematics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 58.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu College Physics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 53.9 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Computer Security |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 80.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Conceptual Physics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 77.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Electrical Engineering |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 76.6 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Elementary Mathematics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 65.6 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Biology |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 86.1 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Chemistry |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 70.4 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Computer Science |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 86.0 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Mathematics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 42.6 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Physics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 62.9 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu High School Statistics |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 71.3 |
| name: accuracy |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: Mmlu Machine Learning |
| type: cais/mmlu |
| metrics: |
| - type: acc |
| value: 57.1 |
| name: accuracy |
|
|
| pipeline_tag: text-generation |
|
|
| library_name: transformers |
|
|
| license: apache-2.0 |
|
|
| license_link: https://huggingface.co/hadadxyz/Qwen3-4B-Diversity/blob/main/LICENSE |
| --- |
| |
| # Introduction |
|
|
|  |
|
|
| Qwen3-4B-Diversity is a fine-tuned language model based on [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) that has been trained on a diverse collection of high-quality reasoning datasets. This model combines knowledge distilled from various state-of-the-art AI systems to provide enhanced reasoning capabilities across multiple domains including mathematics, coding, general problem-solving, and multi-turn conversations. |
|
|
| ### Training Configuration |
|
|
| The model was trained using supervised fine-tuning techniques with parameter-efficient methods to optimize performance while maintaining computational efficiency. Key training parameters include: |
|
|
| | Parameter | Value | |
| |------------------|--------| |
| | Number of Epochs | 2 | |
| | Context Length | 40,960 | |
|
|
| ### Hardware and Resources |
|
|
| | Resource | Specification | |
| |-------------------|------------------------| |
| | GPU | A100-80GB | |
| | Training Duration | Approximately 17 hours | |
| | Estimated Cost | $27 to $30 | |
|
|
| ### Training Data |
|
|
| | Dataset | Rows Used | Model | |
| |--------------------------------------------------------------------------------------------------------------------------------------------|------------|------------------------------------| |
| | [ianncity/KIMI-K2.5-550000x](https://huggingface.co/datasets/ianncity/KIMI-K2.5-550000x) (General-Distillation) | 1,000 | Kimi K2.5 | |
| | [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) | 633 | Qwen3.5 | |
| | [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | 2,326 | Claude Opus 4.6 | |
| | [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x) | 250 | Claude Opus 4.5 | |
| | [TeichAI/gemini-3-pro-preview-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/gemini-3-pro-preview-high-reasoning-250x) | 248 | Gemini 3 Pro | |
| | [TeichAI/claude-haiku-4.5-high-reasoning-1700x](https://huggingface.co/datasets/TeichAI/claude-haiku-4.5-high-reasoning-1700x) | 1,688 | Claude Haiku 4.5 | |
| | [TeichAI/gpt-5.2-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/gpt-5.2-high-reasoning-250x) | 249 | GPT-5.2 | |
| | [Roman1111111/gemini-3.1-pro-hard-high-reasoning](https://huggingface.co/datasets/Roman1111111/gemini-3.1-pro-hard-high-reasoning) | 3,150 | Gemini 3.1 Pro | |
| | [Jackrong/glm-4.7-multiturn-CoT](https://huggingface.co/datasets/Jackrong/glm-4.7-multiturn-CoT) | 5,090 | GLM-4.7 | |
| | [bmeyer2025/glm5-reasoning-traces](https://huggingface.co/datasets/bmeyer2025/glm5-reasoning-traces) | 1,744 | GLM-5 | |
| | [TeichAI/claude-sonnet-4.5-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-sonnet-4.5-high-reasoning-250x) | 247 | Claude Sonnet 4.5 | |
| | [TeichAI/deepseek-v3.2-speciale-openr1-math-3k](https://huggingface.co/datasets/TeichAI/deepseek-v3.2-speciale-openr1-math-3k) | 3,317 | DeepSeek V3.2-Speciale | |
| | [TeichAI/deepseek-v3.2-speciale-OpenCodeReasoning-3k](https://huggingface.co/datasets/TeichAI/deepseek-v3.2-speciale-OpenCodeReasoning-3k) | 2,953 | DeepSeek V3.2-Speciale | |
| | [TeichAI/deepseek-v3.2-speciale-1000x](https://huggingface.co/datasets/TeichAI/deepseek-v3.2-speciale-1000x) | 991 | DeepSeek V3.2-Speciale | |
| | [TeichAI/gpt-5-codex-1000x](https://huggingface.co/datasets/TeichAI/gpt-5-codex-1000x) | 991 | GPT-5 Codex | |
| | **Total** | **24,877** | Combined diverse reasoning dataset | |
|
|
| ## Model Capabilities |
|
|
| This model excels in several key areas: |
|
|
| 1. **Advanced Reasoning**: The model can break down complex problems into steps and provide detailed reasoning processes. |
|
|
| 2. **Mathematical Problem Solving**: Enhanced capabilities for mathematical reasoning and problem-solving through dedicated math-focused datasets. |
|
|
| 3. **Code Generation and Understanding**: Improved coding abilities from multiple code-reasoning datasets including DeepSeek and GPT-5 Codex data. |
|
|
| 4. **Multi-Turn Conversations**: Better handling of extended dialogues and context-aware responses. |
|
|
| 5. **Domain Versatility**: Exposure to reasoning patterns from various AI systems provides flexibility across different domains and task types. |
|
|
| ## Usage |
|
|
| ### Quick Demo |
|
|
| If you are looking for a quick demo that is completely free and without any cost, you can use [Google Colab](https://colab.research.google.com/drive/1qy1n9MigDuwT0cA1Y6ImHChAIlsZPIcC). |
|
|
| ### Ollama (Local) |
|
|
| ```bash |
| # https://ollama.com/hadad/qwen3-4bd |
| |
| # hadad/qwen3-4bd:Q8_0 | 4.3GB |
| # hadad/qwen3-4bd:BF16 | 8.1GB |
| |
| # ollama pull hadad/qwen3-4bd:Q8_0 |
| |
| ollama run hadad/qwen3-4bd:Q8_0 |
| ``` |
|
|
| If you are using Ollama and are interested in **tools** or **function calling**, it is recommended to use the **OpenAI-compatible API** provided by Ollama. This approach is more powerful. |
|
|
| Refer to the [Ollama documentation](https://docs.ollama.com/api/openai-compatibility). |
|
|
| ### Python (Local) |
|
|
| ```bash |
| #pip install transformers==4.56.2 |
| ``` |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_name = "hadadxyz/Qwen3-4B-Diversity" |
| |
| # load the tokenizer and the model |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| |
| # prepare the model input |
| prompt = "Give me a short introduction to large language model." |
| 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. |
| ) |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| |
| # conduct text completion |
| generated_ids = model.generate( |
| **model_inputs, |
| max_new_tokens=32768 |
| ) |
| output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
| |
| # parsing thinking content |
| try: |
| # rindex finding 151668 (</think>) |
| index = len(output_ids) - output_ids[::-1].index(151668) |
| except ValueError: |
| index = 0 |
| |
| thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") |
| content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") |
| |
| print("thinking content:", thinking_content) |
| print("content:", content) |
| ``` |
|
|
| ## Inference Parameters |
|
|
| For optimal results, we recommend the following generation parameters: |
|
|
| ### Thinking |
|
|
| | Parameter | Recommended Value | Description | |
| |-----------------|-------------------|------------------------------------------| |
| | temperature | 0.6 | Controls randomness in generation | |
| | top_p | 0.95 | Nucleus sampling threshold | |
| | top_k | 20 | Top-k sampling parameter | |
| | min_p | 0 | Minimum probability threshold | |
| |
| ### Non-Thinking |
| |
| | Parameter | Recommended Value | Description | |
| |-----------------|-------------------|------------------------------------------| |
| | temperature | 0.7 | Controls randomness in generation | |
| | top_p | 0.8 | Nucleus sampling threshold | |
| | top_k | 20 | Top-k sampling parameter | |
| | min_p | 0 | Minimum probability threshold | |
|
|
| ## Citation |
|
|
| If you use this model in your research or applications, please cite both this model and the base model: |
|
|
| ```bibtex |
| @misc{qwen3-4b-diversity, |
| author = {hadadxyz}, |
| title = {Qwen3-4B-Diversity}, |
| year = {2026}, |
| url = {https://huggingface.co/hadadxyz/Qwen3-4B-Diversity} |
| } |
| ``` |
|
|
| ## Acknowledgments |
|
|
| This model was made possible through the combination of multiple high-quality datasets from the community. We acknowledge and thank all dataset creators and the Qwen team for providing the excellent base model. |
