The gakhg/dpo-qwen-cot-merged model is a 4 billion parameter language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO). It is specifically optimized to enhance reasoning capabilities (Chain-of-Thought) and improve the quality of structured responses. This model is designed for applications requiring robust logical inference and well-formed outputs, leveraging its 40960-token context length.
Loading preview...
Model Overview
This model, gakhg/dpo-qwen-cot-merged, is a 4 billion parameter language model derived from Qwen/Qwen3-4B-Instruct-2507. It has undergone Direct Preference Optimization (DPO) using 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 inference.
- Structured Response Quality: Focuses on generating higher quality, more structured outputs based on preference datasets.
- Full-Merged Weights: Provided as full-merged 16-bit weights, eliminating the need for adapter loading and simplifying deployment with
transformers.
Training Details
The model was fine-tuned 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 training. The LoRA configuration (r=8, alpha=16) was merged into the base model.
Good For
- Applications requiring improved logical reasoning and problem-solving.
- Generating structured and coherent text responses.
- Use cases where direct preference alignment is beneficial for output quality.
Licensing
This model uses the u-10bei/dpo-dataset-qwen-cot for training, which is under the MIT License. Users must also comply with the original base model's license terms.