kikansha-Tomasu/sft-dpo-qwen-cot-merged
kikansha-Tomasu/sft-dpo-qwen-cot-merged is a 4 billion parameter causal language model, fine-tuned from kikansha-Tomasu/Qwen3-4B-Instruct-2507-SFT using Direct Preference Optimization (DPO). This model specializes in improving reasoning capabilities through Chain-of-Thought (CoT) and enhancing structured response quality. It is optimized for tasks requiring aligned, high-quality outputs, particularly in reasoning and structured generation.
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Model Overview
This model, kikansha-Tomasu/sft-dpo-qwen-cot-merged, is a 4 billion parameter language model derived from kikansha-Tomasu/Qwen3-4B-Instruct-2507-SFT. It has been further fine-tuned using Direct Preference Optimization (DPO) via the Unsloth library, with its LoRA adapters merged into the base model for direct use.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, leading to more logical and structured problem-solving.
- Improved Response Quality: Aligned through DPO to generate preferred outputs, focusing on high-quality and structured responses.
- Direct Usage: Provided as a full-merged 16-bit model, eliminating the need for adapter loading and simplifying deployment with
transformers.
Training Details
The model underwent 1 epoch of DPO training 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 preference dataset used for DPO was [u-10bei/dpo-dataset-qwen-cot].
Licensing
This model operates under the MIT License, consistent with its training data. Users are also required to adhere to the licensing terms of the original base model.