nyannto/dpo-qwen-cot-merged
nyannto/dpo-qwen-cot-merged is a fine-tuned Qwen3-4B-Instruct-2507 model, optimized using Direct Preference Optimization (DPO) via Unsloth. This 4 billion parameter model focuses on enhancing reasoning capabilities through Chain-of-Thought (CoT) and improving structured response quality. It is designed for applications requiring aligned and high-quality generative text outputs.
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Overview
This model, nyannto/dpo-qwen-cot-merged, is a specialized fine-tune of the Qwen/Qwen3-4B-Instruct-2507 base model. It leverages Direct Preference Optimization (DPO), implemented with the Unsloth library, to align its outputs with preferred responses. The repository provides the full-merged 16-bit weights, eliminating the need for adapter loading.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, leading to more structured and logical outputs.
- Improved Response Quality: DPO training specifically targets better alignment and higher quality in generated text.
- Direct Use: As a merged model, it can be used directly with the
transformerslibrary without additional configuration.
Training Details
The model was trained for 1 epoch with a learning rate of 5e-07 and a beta value of 0.1, using a maximum sequence length of 1024. The training utilized a specific DPO dataset (u-10bei/dpo-dataset-qwen-cot).
Good For
- Applications requiring models with strong reasoning abilities.
- Scenarios where high-quality, aligned, and structured text generation is crucial.
- Developers seeking a readily deployable Qwen3-4B variant with DPO benefits.