tanliboy/lambda-qwen2.5-14b-dpo-test

The tanliboy/lambda-qwen2.5-14b-dpo-test is a 14.8 billion parameter language model fine-tuned from Qwen/Qwen2.5-14B-Instruct. This model utilizes a large 131,072 token context length and has been optimized using Direct Preference Optimization (DPO) on the HuggingFaceH4/ultrafeedback_binarized dataset. It is designed for tasks requiring nuanced understanding and generation based on human preferences, demonstrating improved reward metrics over its base model.