Model Overview

The oberon1999/dpo-qwen-cot-merged model is a 4 billion parameter language model built upon the Qwen3-4B-Instruct-2507 base. It has undergone Direct Preference Optimization (DPO) using the Unsloth library, specifically targeting improvements in reasoning and structured response generation.

Key Capabilities

• Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, making it suitable for complex problem-solving tasks.
• Structured Output Quality: Fine-tuned to produce higher quality, more aligned structured responses based on preference data.
• Full-Merged Weights: Provided as full-merged 16-bit weights, eliminating the need for adapter loading and simplifying deployment.

Training Details

The model was trained for 1 epoch with a learning rate of 1e-07 and a beta value of 0.1. It utilized a maximum sequence length of 1024 during DPO, leveraging the u-10bei/dpo-dataset-qwen-cot for preference alignment. The LoRA configuration (r=8, alpha=16) was merged into the base model.

Good For

• Applications requiring improved logical reasoning and step-by-step thought processes.
• Generating well-structured and coherent text outputs.
• Tasks where response quality and alignment with preferred examples are critical.