# RAG Enhancement

This is a very compact and fast Japanese reranking model, suitable for improving the accuracy of RAG systems and can run efficiently on CPUs or edge devices.

Qwen2.5-Aloe-Beta-7B is an open-source large medical language model that achieves state-of-the-art performance in multiple medical tasks. It is fine-tuned based on the Qwen2.5-7B architecture, and the training data covers 1.8 billion tokens of diverse medical tasks.

Nemotron-Mini-4B-Instruct is a response generation model developed by NVIDIA, optimized for role-playing, retrieval-augmented generation, and function calling. It is fine-tuned based on Minitron-4B-Base and supports a context length of 4096 tokens.

A lightweight agent model fine-tuned based on TinyLlama, focusing on tool calls, RAG, and general assistant functions