tatsuji1962/dpo-qwen-cot-merged
The tatsuji1962/dpo-qwen-cot-merged model is a 4 billion parameter Qwen3-4B-Instruct-2507 variant, fine-tuned using Direct Preference Optimization (DPO) with the Unsloth library. It is specifically optimized to enhance reasoning capabilities through Chain-of-Thought (CoT) and improve the quality of structured responses. This model is designed for applications requiring precise, aligned outputs, particularly in complex reasoning tasks.
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Model Overview
The tatsuji1962/dpo-qwen-cot-merged model is a specialized 4 billion parameter language model derived from Qwen/Qwen3-4B-Instruct-2507. It has undergone Direct Preference Optimization (DPO) using the Unsloth library, integrating the full 16-bit weights directly without requiring adapter loading.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, making it suitable for tasks requiring logical progression and problem-solving.
- Improved Structured Responses: Fine-tuned to produce higher quality and more aligned structured outputs based on preference datasets.
- Direct Usage: As a fully merged model, it can be used directly with the
transformerslibrary, 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, using a maximum sequence length of 1024. The training leveraged the u-10bei/dpo-dataset-qwen-cot dataset, focusing on aligning model responses with preferred outputs. The base model's license terms must be followed, and the merged model itself is released under the MIT License.