mhseo2/Qwen3-8B-Base-OT3Math-SFT-step500
mhseo2/Qwen3-8B-Base-OT3Math-SFT-step500 is an 8 billion parameter language model, an intermediate checkpoint fine-tuned from Qwen/Qwen3-8B-Base. It is specialized for mathematical reasoning tasks, trained on the knowledge-distillation/openthoughts3_math dataset with a context length of 32768 tokens. The model is designed to produce detailed reasoning traces in a ... format, making it suitable for applications requiring step-by-step mathematical problem-solving.
Loading preview...
Overview
mhseo2/Qwen3-8B-Base-OT3Math-SFT-step500 is an 8 billion parameter language model derived from Qwen/Qwen3-8B-Base. This is an intermediate checkpoint, representing step 500 of a planned 2000-step supervised fine-tuning process. Its primary focus is on enhancing mathematical reasoning capabilities.
Key Capabilities
- Mathematical Reasoning: Specifically fine-tuned on the
knowledge-distillation/openthoughts3_mathdataset, which consists of over 100,000 two-turn mathematical conversations. - Detailed Thinking Process: The model is trained to generate responses that include a
<think>...</think>reasoning trace, culminating in a\boxed{}answer, providing transparent problem-solving steps. - Extended Context Window: Supports a sequence length of 32768 tokens, accommodating complex mathematical problems and detailed reasoning.
- ChatML Compatibility: Utilizes a plain ChatML template, allowing the
<think>blocks to be learned as ordinary text without requiring special thinking logic flags.
Good for
- Applications requiring detailed, step-by-step mathematical problem-solving.
- Generating explanations for mathematical solutions.
- Tasks where understanding the reasoning process is as important as the final answer.
- Research and development in mathematical AI assistants.