INFO8006 Introduction to Artificial Intelligence is a course at ULiège that covers various topics in AI such as intelligent agents, problem-solving, games, probabilistic reasoning, machine learning, neural networks, reinforcement learning, and decision-making. The course includes lectures, exercises, and programming projects using Python. Students can access course materials, previous exams, and archived lectures to enhance their understanding of AI concepts.

Awesome Large Weather Models (LWMs) is a curated collection of articles and resources related to large weather models used in AI for Earth and AI for Science. It includes information on various cutting-edge weather forecasting models, benchmark datasets, and research papers. The repository serves as a hub for researchers and enthusiasts to explore the latest advancements in weather modeling and forecasting.

Awesome Mobile LLMs is a curated list of Large Language Models (LLMs) and related studies focused on mobile and embedded hardware. The repository includes information on various LLM models, deployment frameworks, benchmarking efforts, applications, multimodal LLMs, surveys on efficient LLMs, training LLMs on device, mobile-related use-cases, industry announcements, and related repositories. It aims to be a valuable resource for researchers, engineers, and practitioners interested in mobile LLMs.

At ZenML, we believe in the importance of annotation and labeling workflows in the machine learning lifecycle. This repository showcases a curated list of open-source data annotation and labeling tools that are actively maintained and fit for purpose. The tools cover various domains such as multi-modal, text, images, audio, video, time series, and other data types. Users can contribute to the list and discover tools for tasks like named entity recognition, data annotation for machine learning, image and video annotation, text classification, sequence labeling, object detection, and more. The repository aims to help users enhance their data-centric workflows by leveraging these tools.

CS7320-AI is a repository containing lecture materials, simple Python code examples, and assignments for the course CS 5/7320 Artificial Intelligence. The code examples cover various chapters of the textbook 'Artificial Intelligence: A Modern Approach' by Russell and Norvig. The repository focuses on basic AI concepts rather than advanced implementation techniques. It includes HOWTO guides for installing Python, working on assignments, and using AI with Python.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

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.

The 'crazyai-ml' repository is a collection of resources related to machine learning, specifically focusing on explaining artificial intelligence models. It includes articles, code snippets, and tutorials covering various machine learning algorithms, data analysis, model training, and deployment. The content aims to provide a comprehensive guide for beginners in the field of AI, offering practical implementations and insights into popular machine learning packages and model tuning techniques. The repository also addresses the integration of AI models and frontend-backend concepts, making it a valuable resource for individuals interested in AI applications.

InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM. It is a vision-language foundation model that can perform various tasks, including: **Visual Perception** - Linear-Probe Image Classification - Semantic Segmentation - Zero-Shot Image Classification - Multilingual Zero-Shot Image Classification - Zero-Shot Video Classification **Cross-Modal Retrieval** - English Zero-Shot Image-Text Retrieval - Chinese Zero-Shot Image-Text Retrieval - Multilingual Zero-Shot Image-Text Retrieval on XTD **Multimodal Dialogue** - Zero-Shot Image Captioning - Multimodal Benchmarks with Frozen LLM - Multimodal Benchmarks with Trainable LLM - Tiny LVLM InternVL has been shown to achieve state-of-the-art results on a variety of benchmarks. For example, on the MMMU image classification benchmark, InternVL achieves a top-1 accuracy of 51.6%, which is higher than GPT-4V and Gemini Pro. On the DocVQA question answering benchmark, InternVL achieves a score of 82.2%, which is also higher than GPT-4V and Gemini Pro. InternVL is open-sourced and available on Hugging Face. It can be used for a variety of applications, including image classification, object detection, semantic segmentation, image captioning, and question answering.

TOPU Md is a simple WhatsApp user bot created by Topu Tech. It offers various features such as multi-device support, AI photo enhancement, downloader commands, hidden NSFW commands, logo commands, anime commands, economy menu, various games, and audio/video editor commands. Users can fork the repo, get a session ID by pairing code, and deploy on Heroku. The bot requires Node version 18.x or higher for optimal performance. Contributions to TOPU-MD are welcome, and the tool is safe for use on WhatsApp and Heroku. The tool is licensed under the MIT License and is designed to enhance the WhatsApp experience with diverse features.

Practical LangChain tutorials for developing LLM applications, including prompt templates, output parsing, chatbots memory, chains, evaluating applications, building agents using LangChain & OpenAI API, retrieval augmented generation with LangChain, documents loading, splitting, vector database & text embeddings, information retrieval, answering questions from documents, chat with files, and introduction to Open AI function calling.

This repository is a part of the 13th iT Help Ironman competition, focusing on exploring explainable artificial intelligence (XAI) in machine learning and deep learning. The content covers the basics of XAI, its applications, cases, challenges, and future directions. It also includes practical machine learning algorithms, model deployment, and integration concepts. The author aims to provide detailed resources on AI and share knowledge with the audience through this competition.

LLM4Opt is a collection of references and papers focusing on applying Large Language Models (LLMs) for diverse optimization tasks. The repository includes research papers, tutorials, workshops, competitions, and related collections related to LLMs in optimization. It covers a wide range of topics such as algorithm search, code generation, machine learning, science, industry, and more. The goal is to provide a comprehensive resource for researchers and practitioners interested in leveraging LLMs for optimization tasks.

visionOS-examples is a repository containing accelerators for Spatial Computing. It includes examples such as Local Large Language Model, Chat Apple Vision Pro, WebSockets, Anchor To Head, Hand Tracking, Battery Life, Countdown, Plane Detection, Timer Vision, and PencilKit for visionOS. The repository showcases various functionalities and features for Apple Vision Pro, offering tools for developers to enhance their visionOS apps with capabilities like hand tracking, plane detection, and real-time cryptocurrency prices.

This repository contains the training code of LLM-QAT for large language models. The work investigates quantization-aware training for LLMs, including quantizing weights, activations, and the KV cache. Experiments were conducted on LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. Significant improvements were observed when quantizing weight, activations, and kv cache to 4-bit, 8-bit, and 4-bit, respectively.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

Sorrentum is an open-source project that aims to combine open-source development, startups, and brilliant students to build machine learning, AI, and Web3 / DeFi protocols geared towards finance and economics. The project provides opportunities for internships, research assistantships, and development grants, as well as the chance to work on cutting-edge problems, learn about startups, write academic papers, and get internships and full-time positions at companies working on Sorrentum applications.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

NanoLLM is a tool designed for optimized local inference for Large Language Models (LLMs) using HuggingFace-like APIs. It supports quantization, vision/language models, multimodal agents, speech, vector DB, and RAG. The tool aims to provide efficient and effective processing for LLMs on local devices, enhancing performance and usability for various AI applications.

This repository contains materials for the Microsoft Learn AI Fundamentals module. It covers the basics of artificial intelligence, machine learning, and data science. The content includes hands-on labs, interactive learning modules, and assessments to help learners understand key concepts and techniques in AI. Whether you are new to AI or looking to expand your knowledge, this module provides a comprehensive introduction to the fundamentals of AI.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

The go2coding.github.io repository is a collection of resources for AI enthusiasts, providing information on AI products, open-source projects, AI learning websites, and AI learning frameworks. It aims to help users stay updated on industry trends, learn from community projects, access learning resources, and understand and choose AI frameworks. The repository also includes instructions for local and external deployment of the project as a static website, with details on domain registration, hosting services, uploading static web pages, configuring domain resolution, and a visual guide to the AI tool navigation website. Additionally, it offers a platform for AI knowledge exchange through a QQ group and promotes AI tools through a WeChat public account.

AI-Notes is a repository dedicated to practical applications of artificial intelligence and deep learning. It covers concepts such as data mining, machine learning, natural language processing, and AI. The repository contains Jupyter Notebook examples for hands-on learning and experimentation. It explores the development stages of AI, from narrow artificial intelligence to general artificial intelligence and superintelligence. The content delves into machine learning algorithms, deep learning techniques, and the impact of AI on various industries like autonomous driving and healthcare. The repository aims to provide a comprehensive understanding of AI technologies and their real-world applications.

Prompt Panel is a tool designed to accelerate the adoption of AI agents by providing a platform where users can run large language models across any inference provider, create custom agent plugins, and use their own data safely. The tool allows users to break free from walled-gardens and have full control over their models, conversations, and logic. With Prompt Panel, users can pair their data with any language model, online or offline, and customize the system to meet their unique business needs without any restrictions.

The 'ai-demos' repository is a collection of example code from presentations focusing on building with AI and LLMs. It serves as a resource for developers looking to explore practical applications of artificial intelligence in their projects. The code snippets showcase various techniques and approaches to leverage AI technologies effectively. The repository aims to inspire and educate developers on integrating AI solutions into their 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.