aobu04/dpo-qwen-cot-merged
The aobu04/dpo-qwen-cot-merged model is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO). It is specifically optimized to improve reasoning capabilities, particularly Chain-of-Thought (CoT), and enhance structured response quality. This model is designed for applications requiring improved logical coherence and adherence to preferred output formats.
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Model Overview
This model, aobu04/dpo-qwen-cot-merged, is a 4 billion parameter language model derived from 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 for direct use with transformers.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, leading to more logical and coherent outputs.
- Structured Response Quality: Fine-tuned to align responses with preferred outputs, improving the quality and structure of generated text.
- Efficient Deployment: Provided as a full-merged model, eliminating the need for adapter loading and simplifying integration.
Training Details
The model underwent 1 epoch of DPO training with a learning rate of 1e-07 and a beta value of 0.1. The maximum sequence length used during training was 1024 tokens. The training utilized the u-10bei/dpo-dataset-qwen-cot dataset.
Good For
- Applications requiring improved logical reasoning and step-by-step thought processes.
- Scenarios where structured and high-quality responses are critical.
- Developers seeking a readily deployable Qwen3-4B variant with enhanced preference alignment.