The tarona/qwen3-4b-dpo-marged_001 model is a 4 billion parameter language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO). This model is specifically optimized to enhance reasoning capabilities through Chain-of-Thought and improve the quality of structured responses. It is designed for tasks requiring aligned and preferred outputs based on its DPO training on a specialized preference dataset.
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Model Overview
tarona/qwen3-4b-dpo-marged_001 is a 4 billion parameter language model derived from Qwen/Qwen3-4B-Instruct-2507. It has undergone Direct Preference Optimization (DPO) using the Unsloth library, resulting in a full-merged 16-bit weight model that requires no adapter loading.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought reasoning processes.
- Structured Response Quality: Focuses on generating higher quality and more structured outputs.
- Preference Alignment: Fine-tuned with DPO to align responses with preferred outputs based on a specific preference dataset (u-10bei/dpo-dataset-qwen-cot).
Training Details
The model was trained for 1 epoch with a learning rate of 1e-07 and a beta value of 0.1. It utilized a maximum sequence length of 1024 during the DPO process. The LoRA configuration (r=8, alpha=16) was merged directly into the base model.
Recommended Use Cases
- Applications requiring improved reasoning abilities.
- Scenarios where structured and high-quality responses are critical.
- Tasks benefiting from a model aligned with specific preferred output styles.