MING is an open-sourced Chinese medical consultation model fine-tuned based on medical instructions. The main functions of the model are as follows: Medical Q&A: answering medical questions and analyzing cases. Intelligent consultation: giving diagnosis results and suggestions after multiple rounds of consultation.

VideoRefer Suite is a tool designed to enhance the fine-grained spatial-temporal understanding capabilities of Video Large Language Models (Video LLMs). It consists of three primary components: Model (VideoRefer) for perceiving, reasoning, and retrieval for user-defined regions at any specified timestamps, Dataset (VideoRefer-700K) for high-quality object-level video instruction data, and Benchmark (VideoRefer-Bench) to evaluate object-level video understanding capabilities. The tool can understand any object within a video.

MathModelAgent is an agent designed specifically for mathematical modeling tasks. It automates the process of mathematical modeling and generates a complete paper that can be directly submitted. The tool features automatic problem analysis, code writing, error correction, and paper writing. It supports various models, offers low costs, and allows customization through prompt inject. The tool is ideal for individuals or teams working on mathematical modeling projects.

StarWhisper is a multi-modal model repository developed under the support of the National Astronomical Observatory-Zhijiang Laboratory. It includes language models, temporal models, and multi-modal models ranging from 7B to 72B. The repository provides pre-trained models and technical reports for tasks such as pulsar identification, light curve classification, and telescope control. It aims to integrate astronomical knowledge using large models and explore the possibilities of solving specific astronomical problems through multi-modal approaches.

LLM-Dojo is an open-source platform for learning and practicing large models, providing a framework for building custom large model training processes, implementing various tricks and principles in the llm_tricks module, and mainstream model chat templates. The project includes an open-source large model training framework, detailed explanations and usage of the latest LLM tricks, and a collection of mainstream model chat templates. The term 'Dojo' symbolizes a place dedicated to learning and practice, borrowing its meaning from martial arts training.

A project that utilizes large models to help with Code Review on Gitlab, aimed at improving development efficiency. The project is customized for Gitlab and is developing a Multi-Agent plugin for collaborative review. It integrates various large models for code security issues and stays updated with the latest Code Review trends. The project architecture is designed to be powerful, flexible, and efficient, with easy integration of different models and high customization for developers.

AnyLabeling is a tool for effortless data labeling with AI support from YOLO and Segment Anything. It combines features from LabelImg and Labelme with an improved UI and auto-labeling capabilities. Users can annotate images with polygons, rectangles, circles, lines, and points, as well as perform auto-labeling using YOLOv5 and Segment Anything. The tool also supports text detection, recognition, and Key Information Extraction (KIE) labeling, with multiple language options available such as English, Vietnamese, and Chinese.

ComfyUI GeminiOllama Extension integrates Google's Gemini API, OpenAI (ChatGPT), Anthropic's Claude, Ollama, Qwen, and image processing tools into ComfyUI for leveraging powerful models and features directly within workflows. Features include multiple AI API integrations, advanced prompt engineering, Gemini image generation, background removal, SVG conversion, FLUX resolutions, ComfyUI Styler, smart prompt generator, and more. The extension offers comprehensive API integration, advanced prompt engineering with researched templates, high-quality tools like Smart Prompt Generator and BRIA RMBG, and supports video & audio processing. It provides a single interface to access powerful AI models, transform prompts into detailed instructions, and use various tools for image processing, styling, and content generation.

PandaWiki is a collaborative platform for creating and editing wiki pages. It allows users to easily collaborate on documentation, knowledge sharing, and information dissemination. With features like version control, user permissions, and rich text editing, PandaWiki simplifies the process of creating and managing wiki content. Whether you are working on a team project, organizing information for personal use, or building a knowledge base for your organization, PandaWiki provides a user-friendly and efficient solution for creating and maintaining wiki pages.

EasyAi is a lightweight, beginner-friendly Java artificial intelligence algorithm framework. It can be seamlessly integrated into Java projects with Maven, requiring no additional environment configuration or dependencies. The framework provides pre-packaged modules for image object detection and AI customer service, as well as various low-level algorithm tools for deep learning, machine learning, reinforcement learning, heuristic learning, and matrix operations. Developers can easily develop custom micro-models tailored to their business needs.

AI Paint Today is an API server repository that allows users to record their emotions and daily experiences, and based on that, AI generates a beautiful picture diary of their day. The project includes features such as generating picture diaries from written entries, utilizing DALL-E 2 model for image generation, and deploying on AWS and Cloudflare. The project also follows specific conventions and collaboration strategies for development.

VideoLLaMA 2 is a project focused on advancing spatial-temporal modeling and audio understanding in video-LLMs. It provides tools for multi-choice video QA, open-ended video QA, and video captioning. The project offers model zoo with different configurations for visual encoder and language decoder. It includes training and evaluation guides, as well as inference capabilities for video and image processing. The project also features a demo setup for running a video-based Large Language Model web demonstration.

The project aims to create a personalized AI character, a lifelike AI companion - shaping the ideal image of TA in your mind through your data imprint. The project is inspired by neuro sama, hence named my-neuro. The project can train voice, personality, and replace images. It serves as a workspace where you can use packaged tools to step by step draw and realize the ideal AI image in your mind. The deployment of the current document requires less than 6GB of VRAM, compatible with Windows systems, and requires an API-KEY. The project offers features like low latency, real-time interruption, emotion simulation, visual capabilities integration, voice model training support, desktop control, live streaming on platforms like Bilibili, and more. It aims to provide a comprehensive AI experience with features like long-term memory, AI customization, and emotional interactions.

This project is an intelligent dialogue customer service tool based on a large model, which supports access to platforms such as WeChat, Qianniu, Bilibili, Douyin Enterprise, Douyin, Doudian, Weibo chat, Xiaohongshu professional account operation, Xiaohongshu, Zhihu, etc. You can choose GPT3.5/GPT4.0/ Lazy Treasure Box (more platforms will be supported in the future), which can process text, voice and pictures, and access external resources such as operating systems and the Internet through plug-ins, and support enterprise AI applications customized based on their own knowledge base.

Simple Transformers is a library based on the Transformers library by HuggingFace, allowing users to quickly train and evaluate Transformer models with only 3 lines of code. It supports various tasks such as Information Retrieval, Language Models, Encoder Model Training, Sequence Classification, Token Classification, Question Answering, Language Generation, T5 Model, Seq2Seq Tasks, Multi-Modal Classification, and Conversational AI.

Awesome-Lists is a curated list of awesome lists across various domains of computer science and beyond, including programming languages, web development, data science, and more. It provides a comprehensive index of articles, books, courses, open source projects, and other resources. The lists are organized by topic and subtopic, making it easy to find the information you need. Awesome-Lists is a valuable resource for anyone looking to learn more about a particular topic or to stay up-to-date on the latest developments in the field.

MING is an open-sourced Chinese medical consultation model fine-tuned based on medical instructions. The main functions of the model are as follows: Medical Q&A: answering medical questions and analyzing cases. Intelligent consultation: giving diagnosis results and suggestions after multiple rounds of consultation.

MedicalGPT is a training medical GPT model with ChatGPT training pipeline, implement of Pretraining, Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning) and DPO(Direct Preference Optimization).

Apollo is a multilingual medical LLM that covers English, Chinese, French, Hindi, Spanish, Hindi, and Arabic. It is designed to democratize medical AI to 6B people. Apollo has achieved state-of-the-art results on a variety of medical NLP tasks, including question answering, medical dialogue generation, and medical text classification. Apollo is easy to use and can be integrated into a variety of applications, making it a valuable tool for healthcare professionals and researchers.

The repository 'LLM-for-Healthcare' provides a comprehensive survey of large language models (LLMs) for healthcare, covering data, technology, applications, and accountability and ethics. It includes information on various LLM models, training data, evaluation methods, and computation costs. The repository also discusses tasks such as NER, text classification, question answering, dialogue systems, and generation of medical reports from images in the healthcare domain.

HuatuoGPT-o1 is a medical language model designed for advanced medical reasoning. It can identify mistakes, explore alternative strategies, and refine answers. The model leverages verifiable medical problems and a specialized medical verifier to guide complex reasoning trajectories and enhance reasoning through reinforcement learning. The repository provides access to models, data, and code for HuatuoGPT-o1, allowing users to deploy the model for medical reasoning tasks.

MING is an open-sourced Chinese medical consultation model fine-tuned based on medical instructions. The main functions of the model are as follows: Medical Q&A: answering medical questions and analyzing cases. Intelligent consultation: giving diagnosis results and suggestions after multiple rounds of consultation.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

MedicalGPT is a training medical GPT model with ChatGPT training pipeline, implement of Pretraining, Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning) and DPO(Direct Preference Optimization).

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It provides a comprehensive set of tools for medical image analysis, including data preprocessing, model training, and evaluation. MONAI is designed to be flexible and easy to use, making it a valuable resource for researchers and developers in the field of medical imaging.

Apollo is a multilingual medical LLM that covers English, Chinese, French, Hindi, Spanish, Hindi, and Arabic. It is designed to democratize medical AI to 6B people. Apollo has achieved state-of-the-art results on a variety of medical NLP tasks, including question answering, medical dialogue generation, and medical text classification. Apollo is easy to use and can be integrated into a variety of applications, making it a valuable tool for healthcare professionals and researchers.

CareGPT is a medical large language model (LLM) that explores medical data, training, and deployment related research work. It integrates resources, open-source models, rich data, and efficient deployment methods. It supports various medical tasks, including patient diagnosis, medical dialogue, and medical knowledge integration. The model has been fine-tuned on diverse medical datasets to enhance its performance in the healthcare domain.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.