Model Overview

This model, dpo-qwen-cot-merged_120steps, is a 4 billion parameter language model developed by D-AT2025. It is a fine-tuned version of the Qwen/Qwen3-4B-Instruct-2507 base model, utilizing Direct Preference Optimization (DPO) via the Unsloth library. The primary objective of this fine-tuning was to align the model's responses with preferred outputs, specifically focusing on improving reasoning (Chain-of-Thought) and the overall quality of structured responses.

Key Capabilities

• Enhanced Reasoning: Optimized for better Chain-of-Thought capabilities.
• Improved Response Quality: Aligned to produce higher quality, structured outputs.
• DPO Fine-tuning: Benefits from Direct Preference Optimization for preference alignment.
• Merged Weights: Provided as full-merged 16-bit weights, eliminating the need for adapter loading.

Training Details

The model was trained for 1 epoch with a learning rate of 1e-07 and a beta value of 0.1. The maximum sequence length used during training was 1024. The training data utilized for DPO was u-10bei/dpo-dataset-qwen-cot.

Good For

• Applications requiring improved reasoning abilities.
• Tasks demanding high-quality, structured text generation.
• Scenarios where preference alignment is crucial for model output.