hiro7ka/dpo-qwen-cot-merged-ver3d
The hiro7ka/dpo-qwen-cot-merged-ver3d is a 4 billion parameter language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO). This model is specifically optimized to enhance reasoning capabilities through Chain-of-Thought (CoT) and improve structured response quality. It excels in generating aligned and preferred outputs for complex reasoning tasks, making it suitable for applications requiring high-quality, structured text generation.
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Model Overview
This model, hiro7ka/dpo-qwen-cot-merged-ver3d, 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, resulting in a merged 16-bit weight model that requires no adapter loading.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, allowing for more structured and logical response generation.
- Improved Response Quality: Fine-tuned to align outputs with preferred responses, leading to higher quality and more relevant text.
- Direct Use: As a fully merged model, it can be used directly with the
transformerslibrary for inference.
Training Details
The model was trained for 0.5 epochs with a learning rate of 1e-08 and a beta value of 0.1, using a maximum sequence length of 1024. The DPO training leveraged the u-10bei/dpo-dataset-qwen-cot dataset. Users should adhere to the MIT License of the dataset and the original base model's license terms.