ToshiyaOg/dpo-qwen-cot-merged
ToshiyaOg/dpo-qwen-cot-merged 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 improve reasoning capabilities through Chain-of-Thought (CoT) and enhance the quality of structured responses. It is designed for tasks requiring aligned, high-quality outputs based on preferred response patterns.
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Overview
This model, ToshiyaOg/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, resulting in a full-merged 16-bit weight model that requires no adapter loading.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, leading to more structured and logical outputs.
- Aligned Responses: Fine-tuned with DPO to align its generated text with preferred response patterns, improving overall output quality.
- Structured Output: Focuses on generating high-quality, structured responses based on the training objective.
Training Details
The model was trained for 1 epoch with a learning rate of 2e-06 and a beta value of 0.1, using a maximum sequence length of 1024. The training utilized the u-10bei/dpo-dataset-qwen-cot dataset, specifically designed for DPO with Chain-of-Thought preferences.
Good For
- Applications requiring improved reasoning and logical flow in generated text.
- Scenarios where aligned and high-quality structured responses are critical.
- Developers looking for a Qwen3-4B variant with enhanced preference alignment and CoT capabilities.