Hi-Satoh/adv_sft_dpo_final_3_merged
The Hi-Satoh/adv_sft_dpo_final_3_merged 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 improve reasoning capabilities, particularly Chain-of-Thought, and enhance structured response quality. It is designed for applications requiring aligned and coherent outputs based on preferred response patterns.
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Model Overview
Hi-Satoh/adv_sft_dpo_final_3_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 into the base model.
Key Capabilities
- Enhanced Reasoning: Optimized to improve Chain-of-Thought reasoning, enabling more structured and logical outputs.
- Improved Response Quality: DPO training aligns the model's responses with preferred outputs, leading to higher quality and more coherent generations.
- Structured Output Generation: Focuses on generating structured responses based on preference datasets.
Training Details
The model underwent 1 epoch of DPO training with a learning rate of 7e-06 and a beta value of 0.5. It utilized a maximum sequence length of 4096 tokens. The training data used was [Hi-Satoh/test_dpo_dataset].
Good For
- Applications requiring models with strong reasoning abilities.
- Scenarios where structured and aligned outputs are critical.
- Tasks benefiting from preference-tuned response generation.