SomasE/dpo-qwen-cot-merged
SomasE/dpo-qwen-cot-merged is a 4 billion parameter causal language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO) via Unsloth. This model is specifically optimized to enhance reasoning capabilities through Chain-of-Thought (CoT) and improve structured response quality. It is designed for tasks requiring aligned and coherent outputs based on preferred response patterns.
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Model Overview
SomasE/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, with its 16-bit weights fully merged for direct use without adapters.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, leading to more structured and logical outputs.
- Improved Response Quality: Fine-tuned to align responses with preferred outputs, enhancing overall coherence and quality.
- DPO Alignment: Leverages DPO to refine model behavior based on a preference dataset, ensuring outputs meet specific criteria.
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 and LoRA configuration (r=8, alpha=16) which has been merged into the base model. The training data used was u-10bei/dpo-dataset-qwen-cot.
Good For
- Applications requiring models with strong reasoning abilities.
- Scenarios where structured and high-quality responses are critical.
- Tasks benefiting from DPO-aligned outputs.