SKOTK/dpo-qwen-cot-merged
SKOTK/dpo-qwen-cot-merged is a 4 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO). This model is specifically optimized to enhance reasoning capabilities, particularly Chain-of-Thought (CoT), and improve the quality of structured responses. It is designed for tasks requiring aligned and coherent outputs based on preference datasets.
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Model Overview
SKOTK/dpo-qwen-cot-merged is a 4 billion parameter language model derived from Qwen/Qwen3-4B-Instruct-2507. It has undergone fine-tuning using Direct Preference Optimization (DPO) via the Unsloth library, resulting in a full-merged 16-bit model that requires no adapter loading.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought (CoT) reasoning, making it suitable for complex problem-solving tasks.
- Structured Response Quality: Fine-tuned to produce higher quality and more aligned structured outputs based on preferred examples.
- DPO Alignment: Benefits from DPO training, aligning its responses more closely with desired human preferences.
Training Details
The model was trained for 1 epoch with a learning rate of 1e-07 and a beta value of 0.1, using a maximum sequence length of 1024. The training utilized a specific preference dataset (u-10bei/dpo-dataset-qwen-cot) to guide the DPO process. The base model's license terms (MIT License) apply.
Good For
- Applications requiring improved reasoning and logical coherence in responses.
- Generating structured outputs that adhere to specific formats or preferences.
- Tasks where alignment with human preferences is crucial for output quality.