aliRafik/Qwen3_4B_Thinking_Tuning_Data_Unsloth_OpenMathReasoning_ANd_Modotte_MathX_5M_16bit
aliRafik/Qwen3_4B_Thinking_Tuning_Data_Unsloth_OpenMathReasoning_ANd_Modotte_MathX_5M_16bit is a 4 billion parameter Qwen3-based language model developed by aliRafik, specifically fine-tuned for enhanced mathematical reasoning and structured problem-solving. It excels at generating logically structured, verifiable, and appropriately explained step-by-step mathematical solutions, trained on datasets like OpenMathReasoning and MathX-5M. This model is optimized to balance accuracy, reasoning, and clarity, adapting explanation length to problem complexity, making it suitable for educational applications and mathematical question answering.
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Qwen3-4B Mathematical Reasoning Model
This model, developed by aliRafik, is a specialized Qwen3-4B variant fine-tuned for advanced mathematical reasoning. It leverages the base Qwen3-4B architecture and was trained using Unsloth and Huggingface's TRL library, achieving faster training times. The primary goal is to produce mathematical answers that are not only correct but also logically structured, verifiable, and clearly explained, adapting the level of detail to the problem's complexity.
Key Capabilities
- Enhanced Mathematical Reasoning: Focuses on structured problem-solving and step-by-step explanations.
- Dataset Training: Fine-tuned on large-scale mathematical reasoning datasets, specifically
unsloth/OpenMathReasoningandModotte/MathX-5M(5 million reasoning samples). - Proportional Reasoning: Designed to balance accuracy, reasoning, verification, clarity, and appropriate explanation length, avoiding unnecessary complexity for simple problems.
- Adaptable Explanations: Can adjust the depth of explanation based on the problem's difficulty, providing concise derivations for simple equations and detailed ones for complex problems.
- 16-bit Precision: Utilizes 16-bit floating-point precision for a balance of numerical accuracy and performance.
Intended Use Cases
- Mathematical Reasoning Research: Ideal for exploring and advancing research in mathematical problem-solving with LLMs.
- Educational Applications: Can be used for mathematical question answering and providing step-by-step assistance.
- LLM Fine-tuning Experiments: Suitable for further experimentation and fine-tuning in mathematical domains.
- Benchmark Experimentation: Useful for evaluating and comparing performance on mathematical benchmarks, considering metrics beyond just final answer accuracy.
While specialized, users should be aware of potential limitations such as arithmetic mistakes or incorrect intermediate reasoning, and solutions for critical applications should always be independently verified.