konagayoshi/dpo-qwen-cot-merged
The konagayoshi/dpo-qwen-cot-merged model is a 4 billion parameter Qwen3-based causal language model, fine-tuned by konagayoshi using Direct Preference Optimization (DPO). It specializes in enhancing reasoning capabilities through Chain-of-Thought (CoT) and improving structured response quality. This model is optimized for tasks requiring logical deduction and coherent, well-organized outputs.
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Overview
This model, konagayoshi/dpo-qwen-cot-merged, is a 4 billion parameter language model built upon the Qwen/Qwen3-4B-Instruct-2507 base. It has been specifically fine-tuned using Direct Preference Optimization (DPO) via the Unsloth library to align its responses with preferred outputs.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, making it suitable for tasks requiring logical steps and deductions.
- Structured Responses: Focuses on generating higher quality, more structured outputs based on a preference dataset.
- Direct Use: Provided as a full-merged 16-bit model, eliminating the need for adapter loading and allowing direct use with the
transformerslibrary.
Training Details
The model underwent 1 epoch of DPO training with a learning rate of 1e-07 and a beta value of 0.1. It utilized a maximum sequence length of 1024 and incorporated LoRA configuration (r=8, alpha=16) which has been merged into the base model. The training data used was u-10bei/dpo-dataset-qwen-cot.
Good For
- Applications requiring improved logical reasoning.
- Generating well-structured and coherent text outputs.
- Developers looking for a Qwen3-based model with enhanced preference alignment for reasoning tasks.