tmaoshima/dpo-qwen-cot-merged
tmaoshima/dpo-qwen-cot-merged is a 4 billion parameter Qwen3-based causal language model, fine-tuned using Direct Preference Optimization (DPO) via Unsloth. This model is specifically optimized to improve reasoning capabilities through Chain-of-Thought (CoT) and enhance structured response quality. It leverages a preference dataset for alignment, making it suitable for tasks requiring coherent and well-reasoned outputs.
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Model Overview
This model, tmaoshima/dpo-qwen-cot-merged, is a 4 billion parameter language model based on Qwen/Qwen3-4B-Instruct-2507. It has been fine-tuned using Direct Preference Optimization (DPO) via the Unsloth library, with its 16-bit weights fully merged into the base model.
Key Capabilities & Optimization
- Enhanced Reasoning: Optimized specifically to improve Chain-of-Thought (CoT) reasoning, leading to more logical and structured responses.
- Improved Response Quality: DPO training aligns the model's outputs with preferred examples, enhancing the overall quality and coherence of generated text.
- Direct Usage: As a fully merged model, it can be used directly with the
transformerslibrary without requiring adapter loading.
Training Details
The model underwent 1 epoch of DPO training with a learning rate of 2e-07 and a beta value of 0.02. It utilized a maximum sequence length of 512 tokens during training. The training data, u-10bei/dpo-dataset-qwen-cot, was instrumental in guiding the preference optimization process.
Licensing
Users must adhere to the MIT License as per the dataset terms and comply with the original base model's license terms.