# Medical QA Optimization

GGUF format re-ranking model converted from BAAI/bge-reranker-base, supporting Chinese and English text sorting tasks

An 8B-parameter medical QA model distilled and optimized based on the Llama architecture, enhanced for clinical decision support through specialized medical data fine-tuning

This model is converted from thenlper/gte-large-zh to GGUF format via llama.cpp, primarily used for Chinese text embedding and sentence similarity calculation.

This model is converted from BAAI/bge-reranker-base into the GGUF format, primarily used for text reordering tasks.

This model is converted from BAAI/bge-reranker-large into GGUF format for reranking tasks, supporting both Chinese and English.

A fine-tuned version based on Minbyul/selfbiorag-7b-wo-kqa_golden-iter-dpo-step3-filtered, trained using the HuggingFace MedLFQA dataset (excluding kqa_golden) with Direct Preference Optimization (DPO)

A 1.1B-parameter conversational language model fine-tuned on medical data, achieving an average 13.76% improvement on medical exam benchmarks

This is a hybrid model based on Llama-2-13B, incorporating features from multiple models including Tiefighter, MedLLaMA, and limarp-v2, suitable for various text generation tasks.