llm_related is a repository that documents issues encountered and solutions found during the application of large models. It serves as a knowledge base for troubleshooting and problem-solving in the context of working with complex models in various applications.

Enterprise h2oGPTe - GenAI RAG is a repository containing code examples, notebooks, and benchmarks for the enterprise version of h2oGPTe, a powerful AI tool for generating text based on the RAG (Retrieval-Augmented Generation) architecture. The repository provides resources for leveraging h2oGPTe in enterprise settings, including implementation guides, performance evaluations, and best practices. Users can explore various applications of h2oGPTe in natural language processing tasks, such as text generation, content creation, and conversational AI.

This repository contains a collection of articles and tutorials on quantitative finance, including topics such as machine learning, statistical arbitrage, and risk management. The articles are written in a clear and concise style, and they are suitable for both beginners and experienced practitioners. The repository also includes a number of Jupyter notebooks that demonstrate how to use Python for quantitative finance.

A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This repository is designed for individuals aiming to excel in the field of Data and AI, providing video sessions and text content for learning. It caters to those in leadership positions, professionals, and students, emphasizing the need for dedicated effort to achieve excellence in the tech field. The content covers various topics with a focus on practical application.

This repository contains a collection of AI projects implemented in Python, primarily in Jupyter notebooks. The projects cover various aspects of artificial intelligence, including machine learning, deep learning, natural language processing, computer vision, and more. Each project is designed to showcase different AI techniques and algorithms, providing a hands-on learning experience for users interested in exploring the field of artificial intelligence.

llm_rl is a repository that combines llm (language model) and rl (reinforcement learning) techniques. It likely focuses on using language models in reinforcement learning tasks, such as natural language understanding and generation. The repository may contain implementations of algorithms that leverage both llm and rl to improve performance in various tasks. Developers interested in exploring the intersection of language models and reinforcement learning may find this repository useful for research and experimentation.

SciML.ai is an open source software organization dedicated to unifying packages for scientific machine learning. It focuses on developing modular scientific simulation support software, including differential equation solvers, inverse problems methodologies, and automated model discovery. The organization aims to provide a diverse set of tools with a common interface, creating a modular, easily-extendable, and highly performant ecosystem for scientific simulations. The website serves as a platform to showcase SciML organization's packages and share news within the ecosystem. Pull requests are encouraged for contributions.

Large Language Models (LLM) are language models built by deep neural networks containing hundreds of billions of weights, trained on a large amount of unlabeled text using self-supervised learning methods. Since 2018, companies and research institutions including Google, OpenAI, Meta, Baidu, and Huawei have released various models such as BERT, GPT, etc., which have performed well in almost all natural language processing tasks. Starting in 2021, large models have shown explosive growth, especially after the release of ChatGPT in November 2022, attracting worldwide attention. Users can interact with systems using natural language to achieve various tasks from understanding to generation, including question answering, classification, summarization, translation, and chat. Large language models demonstrate powerful knowledge of the world and understanding of language. This repository introduces the basic theory of large language models including language models, distributed model training, and reinforcement learning, and uses the Deepspeed-Chat framework as an example to introduce the implementation of large language models and ChatGPT-like systems.

The Atomic Agents framework is a modular and extensible tool designed for creating powerful applications. It leverages Pydantic for data validation and serialization. The framework follows the principles of Atomic Design, providing small and single-purpose components that can be combined. It integrates with Instructor for AI agent architecture and supports various APIs like Cohere, Anthropic, and Gemini. The tool includes documentation, examples, and testing features to ensure smooth development and usage.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

The llm_aigc repository is a comprehensive resource for everything related to llm (Large Language Models) and aigc (AI Governance and Control). It provides detailed information, resources, and tools for individuals interested in understanding and working with large language models and AI governance and control. The repository covers a wide range of topics including model training, evaluation, deployment, ethics, and regulations in the AI field.

This repository contains a collection of resources for learning about and using Large Language Models (LLMs). The resources include tutorials, code examples, and links to additional resources. LLMs are a type of artificial intelligence that can understand and generate human-like text. They have a wide range of potential applications, including natural language processing, machine translation, and chatbot development.

SolarLLMZeroToAll is a comprehensive repository that provides a step-by-step guide and resources for learning and implementing Solar Longitudinal Learning Machines (SolarLLM) from scratch. The repository covers various aspects of SolarLLM, including theory, implementation, and applications, making it suitable for beginners and advanced users interested in solar energy forecasting and machine learning. The materials include detailed explanations, code examples, datasets, and visualization tools to facilitate understanding and practical implementation of SolarLLM models.

The ai-workshop-code repository contains code examples and tutorials for various artificial intelligence concepts and algorithms. It serves as a practical resource for individuals looking to learn and implement AI techniques in their projects. The repository covers a wide range of topics, including machine learning, deep learning, natural language processing, computer vision, and reinforcement learning. By exploring the code and following the tutorials, users can gain hands-on experience with AI technologies and enhance their understanding of how these algorithms work in practice.

This repository contains material related to the new book _Synthetic Data and Generative AI_ by the author, including code for NoGAN, DeepResampling, and NoGAN_Hellinger. NoGAN is a tabular data synthesizer that outperforms GenAI methods in terms of speed and results, utilizing state-of-the-art quality metrics. DeepResampling is a fast NoGAN based on resampling and Bayesian Models with hyperparameter auto-tuning. NoGAN_Hellinger combines NoGAN and DeepResampling with the Hellinger model evaluation metric.

HuggingArxiv is a tool designed to push research papers related to large language models from Arxiv. It helps users stay updated with the latest developments in the field of large language models by providing notifications and access to relevant papers.

llm_related is a repository that documents issues encountered and solutions found during the application of large models. It serves as a knowledge base for troubleshooting and problem-solving in the context of working with complex models in various applications.

This repository contains Jupyter-based tutorials for NLP, ML, AI in Python for classes in Computational Linguistics, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) at Indiana University.

LibreChat is an all-in-one AI conversation platform that integrates multiple AI models, including ChatGPT, into a user-friendly interface. It offers a wide range of features, including multimodal chat, multilingual UI, AI model selection, custom presets, conversation branching, message export, search, plugins, multi-user support, and extensive configuration options. LibreChat is open-source and community-driven, with a focus on providing a free and accessible alternative to ChatGPT Plus. It is designed to enhance productivity, creativity, and communication through advanced AI capabilities.

GPT-Engineer is a tool that allows you to specify a software in natural language, sit back and watch as an AI writes and executes the code, and ask the AI to implement improvements.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

This repository is dedicated to the learning and exchange of resources for the School of Artificial Intelligence at Anhui University. Notes will be published on this website first: https://www.aoaoaoao.cn and will be synchronized to the repository regularly. You can also contact me at [email protected].

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

This repository contains a collection of apps that utilize the astounding AI of ChatGPT or enhance its UX. These apps range from simple scripts to full-fledged extensions, each designed to make your ChatGPT experience more efficient, enjoyable, or private.

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.