MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

Dioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). It supports the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks. Dioptra provides a REST API and can be controlled via a web interface or Python client for designing, managing, executing, and tracking experiments. It aims to be reproducible, traceable, extensible, interoperable, modular, secure, interactive, shareable, and reusable.

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.

The 'LLMs-in-science' repository is a collaborative environment for organizing papers related to large language models (LLMs) and autonomous agents in the field of chemistry. The goal is to discuss trend topics, challenges, and the potential for supporting scientific discovery in the context of artificial intelligence. The repository aims to maintain a systematic structure of the field and welcomes contributions from the community to keep the content up-to-date and relevant.

Parrot is a distributed serving system for LLM-based Applications, designed to efficiently serve LLM-based applications by adding Semantic Variable in the OpenAI-style API. It allows for horizontal scalability with multiple Engine instances running LLM models communicating with ServeCore. The system enables AI agents to interact with LLMs via natural language prompts for collaborative tasks.

CodeFuse-muAgent is a Multi-Agent framework designed to streamline Standard Operating Procedure (SOP) orchestration for agents. It integrates toolkits, code libraries, knowledge bases, and sandbox environments for rapid construction of complex Multi-Agent interactive applications. The framework enables efficient execution and handling of multi-layered and multi-dimensional tasks.

DNAnalyzer is a nonprofit organization dedicated to revolutionizing DNA analysis through AI-powered tools. It aims to democratize access to DNA analysis for a deeper understanding of human health and disease. The tool provides innovative AI-powered analysis and interpretive tools to empower geneticists, physicians, and researchers to gain deep insights into DNA sequences, revolutionizing how we understand human health and disease.

This repository introduces and helps organizations get started with building AI Apps and incorporating Large Language Models (LLMs) into them. The workshop covers topics such as prompt engineering, AI orchestration, and deploying AI apps. Participants will learn how to use Azure OpenAI, Langchain/ Semantic Kernel, Qdrant, and Azure AI Search to build intelligent applications.

AI Summer is a repository focused on providing workshops and resources for developing foundational skills in generative AI models and transformer models. The repository offers practical applications for inferencing and training, with a specific emphasis on understanding and utilizing advanced AI chat models like BingGPT. Participants are encouraged to engage in interactive programming environments, decide on projects to work on, and actively participate in discussions and breakout rooms. The workshops cover topics such as generative AI models, retrieval-augmented generation, building AI solutions, and fine-tuning models. The goal is to equip individuals with the necessary skills to work with AI technologies effectively and securely, both locally and in the cloud.

This repository provides resources, demos, and recipes for working with Large Language Models (LLMs) on OpenShift using OpenShift AI or Open Data Hub. It includes instructions for deploying inference servers for LLMs, such as vLLM, Hugging Face TGI, Caikit-TGIS-Serving, and Ollama. Additionally, it offers guidance on deploying serving runtimes, such as vLLM Serving Runtime and Hugging Face Text Generation Inference, in the Single-Model Serving stack of Open Data Hub or OpenShift AI. The repository also covers vector databases that can be used as a Vector Store for Retrieval Augmented Generation (RAG) applications, including Milvus, PostgreSQL+pgvector, and Redis. Furthermore, it provides examples of inference and application usage, such as Caikit, Langchain, Langflow, and UI examples.

GrAIdient is a framework designed to enable the development of deep learning models using the internal GPU of a Mac. It provides access to the graph of layers, allowing for unique model design with greater understanding, control, and reproducibility. The goal is to challenge the understanding of deep learning models, transitioning from black box to white box models. Key features include direct access to layers, native Mac GPU support, Swift language implementation, gradient checking, PyTorch interoperability, and more. The documentation covers main concepts, architecture, and examples. GrAIdient is MIT licensed.

Merlin is a groundbreaking model capable of generating natural language responses intricately linked with object trajectories of multiple images. It excels in predicting and reasoning about future events based on initial observations, showcasing unprecedented capability in future prediction and reasoning. Merlin achieves state-of-the-art performance on the Future Reasoning Benchmark and multiple existing multimodal language models benchmarks, demonstrating powerful multi-modal general ability and foresight minds.

AI2Apps is a visual IDE for building LLM-based AI agent applications, enabling developers to efficiently create AI agents through drag-and-drop, with features like design-to-development for rapid prototyping, direct packaging of agents into apps, powerful debugging capabilities, enhanced user interaction, efficient team collaboration, flexible deployment, multilingual support, simplified product maintenance, and extensibility through plugins.

This repository contains code for the O'Reilly Live Online Training for Deploying GPT & LLMs. Learn how to use GPT-4, ChatGPT, OpenAI embeddings, and other large language models to build applications for experimenting and production. Gain practical experience in building applications like text generation, summarization, question answering, and more. Explore alternative generative models such as Cohere and GPT-J. Understand prompt engineering, context stuffing, and few-shot learning to maximize the potential of GPT-like models. Focus on deploying models in production with best practices and debugging techniques. By the end of the training, you will have the skills to start building applications with GPT and other large language models.

CSGHub is an open source platform for managing large model assets, including datasets, model files, and codes. It offers functionalities similar to a privatized Huggingface, managing assets in a manner akin to how OpenStack Glance manages virtual machine images. Users can perform operations such as uploading, downloading, storing, verifying, and distributing assets through various interfaces. The platform provides microservice submodules and standardized OpenAPIs for easy integration with users' systems. CSGHub is designed for large models and can be deployed On-Premise for offline operation.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.