AIFoundation focuses on AI Foundation, large model systems. Large models optimize the performance of full-stack hardware and software based on AI clusters. The training process requires distributed parallelism, cluster communication algorithms, and continuous evolution in the field of large models such as intelligent agents. The course covers modules like AI chip principles, communication & storage, AI clusters, computing architecture, communication architecture, large model algorithms, training, inference, and analysis of hot technologies in the large model field.

AIInfra is an open-source project focused on AI infrastructure, specifically targeting large models in distributed clusters, distributed architecture, distributed training, and algorithms related to large models. The project aims to explore and study system design in artificial intelligence and deep learning, with a focus on the hardware and software stack for building AI large model systems. It provides a comprehensive curriculum covering topics such as AI chip principles, communication and storage, AI clusters, large model training, and inference, as well as algorithms for large models. The course is designed for undergraduate and graduate students, as well as professionals working with AI large model systems, to gain a deep understanding of AI computer system architecture and design.

Awesome-AISourceHub is a repository that collects high-quality information sources in the field of AI technology. It serves as a synchronized source of information to avoid information gaps and information silos. The repository aims to provide valuable resources for individuals such as AI book authors, enterprise decision-makers, and tool developers who frequently use Twitter to share insights and updates related to AI advancements. The platform emphasizes the importance of accessing information closer to the source for better quality content. Users can contribute their own high-quality information sources to the repository by following specific steps outlined in the contribution guidelines. The repository covers various platforms such as Twitter, public accounts, knowledge planets, podcasts, blogs, websites, YouTube channels, and more, offering a comprehensive collection of AI-related resources for individuals interested in staying updated with the latest trends and developments in the AI field.

This repository contains the training code of MobileLLM, a language model optimized for on-device use cases with fewer than a billion parameters. It integrates SwiGLU activation function, deep and thin architectures, embedding sharing, and grouped-query attention to achieve high-quality LLMs. MobileLLM-125M/350M shows significant accuracy improvements over previous models on zero-shot commonsense reasoning tasks. The design philosophy scales effectively to larger models, with state-of-the-art results for MobileLLM-600M/1B/1.5B.

LangBot is a highly stable, extensible, and multimodal instant messaging chatbot platform based on large language models. It supports various large models, adapts to group chats and private chats, and has capabilities for multi-turn conversations, tool invocation, and multimodal interactions. It is deeply integrated with Dify and currently supports QQ and QQ channels, with plans to support platforms like WeChat, WhatsApp, and Discord. The platform offers high stability, comprehensive functionality, native support for access control, rate limiting, sensitive word filtering mechanisms, and simple configuration with multiple deployment options. It also features plugin extension capabilities, an active community, and a new web management panel for managing LangBot instances through a browser.

This repository is a curated collection of data science and AI concepts and IQs, covering topics from foundational mathematics to cutting-edge generative AI concepts. It aims to support learners and professionals preparing for various data science roles by providing detailed explanations and notebooks for each concept.

AI Product Home is a repository dedicated to collecting various AI commercial or open-source products. It provides assistance in submitting issues, self-recommendation, correcting resources, and more. The repository also features AI tools like Build Naidia, Autopod, Rytr, Mubert, and a virtual town driven by AI. It includes sections for AI models, chat dialogues, AI assistants, code assistance, artistic creation, content creation, and more. The repository covers a wide range of AI-related tools and resources for users interested in AI products and services.

This repository collects various LLM, AI-related models, applications, and datasets, including LLM-Chat for dialogue models, LLMs for large models, so-vits-svc for sound-related models, stable-diffusion for image-related models, and virtual-digital-person for generating videos. It also provides resources for deep learning courses and overviews, AI competitions, and specific AI tasks such as text, image, voice, and video processing.

The 'step_into_llm' repository is dedicated to the 昇思MindSpore technology open class, which focuses on exploring cutting-edge technologies, combining theory with practical applications, expert interpretations, open sharing, and empowering competitions. The repository contains course materials, including slides and code, for the ongoing second phase of the course. It covers various topics related to large language models (LLMs) such as Transformer, BERT, GPT, GPT2, and more. The course aims to guide developers interested in LLMs from theory to practical implementation, with a special emphasis on the development and application of large models.

PaddleScience is a scientific computing suite developed based on the deep learning framework PaddlePaddle. It utilizes the learning ability of deep neural networks and the automatic (higher-order) differentiation mechanism of PaddlePaddle to solve problems in physics, chemistry, meteorology, and other fields. It supports three solving methods: physics mechanism-driven, data-driven, and mathematical fusion, and provides basic APIs and detailed documentation for users to use and further develop.

Kumo search is an end-to-end search engine framework that supports full-text search, inverted index, forward index, sorting, caching, hierarchical indexing, intervention system, feature collection, offline computation, storage system, and more. It runs on the EA (Elastic automic infrastructure architecture) platform, enabling engineering automation, service governance, real-time data, service degradation, and disaster recovery across multiple data centers and clusters. The framework aims to provide a ready-to-use search engine framework to help users quickly build their own search engines. Users can write business logic in Python using the AOT compiler in the project, which generates C++ code and binary dynamic libraries for rapid iteration of the search engine.

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.

RKLLM software stack is a toolkit designed to help users quickly deploy AI models to Rockchip chips. It consists of RKLLM-Toolkit for model conversion and quantization, RKLLM Runtime for deploying models on Rockchip NPU platform, and RKNPU kernel driver for hardware interaction. The toolkit supports RK3588 and RK3576 series chips and various models like TinyLLAMA, Qwen, Phi, ChatGLM3, Gemma, InternLM2, and MiniCPM. Users can download packages, docker images, examples, and docs from RKLLM_SDK. Additionally, RKNN-Toolkit2 SDK is available for deploying additional AI models.

Indie Hacker Tools Plus is a curated repository of essential tools and technology stacks for independent developers. The repository aims to help developers enhance efficiency, save costs, and mitigate risks by using popular and validated tools. It provides a collection of tools recognized by the industry to empower developers with the most refined technical support. Developers can contribute by submitting articles, software, or resources through issues or pull requests.

This project open sources the **Chinese LLaMA model and the Alpaca large model fine-tuned with instructions**, to further promote the open research of large models in the Chinese NLP community. These models **extend the Chinese vocabulary based on the original LLaMA** and use Chinese data for secondary pre-training, further enhancing the basic Chinese semantic understanding ability. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, significantly improving the model's understanding and execution of instructions.

AlignBench is the first comprehensive evaluation benchmark for assessing the alignment level of Chinese large models across multiple dimensions. It includes introduction information, data, and code related to AlignBench. The benchmark aims to evaluate the alignment performance of Chinese large language models through a multi-dimensional and rule-calibrated evaluation method, enhancing reliability and interpretability.

This repository provides a collection of interview questions and answers for algorithm engineers. The questions are organized by topic, and each question includes a detailed explanation of the answer. This repository is a valuable resource for anyone preparing for an algorithm engineering interview.

PythonPark is a paradise for learning Python, providing babysitter-level tutorials on AI labs, treasure videos, data structures, study guides, machine learning practicals, deep learning practicals, Python basics, web scraping, big company interview experiences, programming life, and resource sharing. Original articles are published at least twice a week, with the latest articles being first released on WeChat and videos on Bilibili. Join the WeChat group for technical discussions or to provide feedback. Continuously improving and outputting content!

The ai-explorables repository contains code for AI Explorables, a tool that allows users to make changes in the source code and view the changes locally. It is not an officially supported Google product.

AIFoundation focuses on AI Foundation, large model systems. Large models optimize the performance of full-stack hardware and software based on AI clusters. The training process requires distributed parallelism, cluster communication algorithms, and continuous evolution in the field of large models such as intelligent agents. The course covers modules like AI chip principles, communication & storage, AI clusters, computing architecture, communication architecture, large model algorithms, training, inference, and analysis of hot technologies in the large model field.

ByteMLPerf is an AI Accelerator Benchmark that focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware. Byte MLPerf has the following characteristics: - Models and runtime environments are more closely aligned with practical business use cases. - For ASIC hardware evaluation, besides evaluate performance and accuracy, it also measure metrics like compiler usability and coverage. - Performance and accuracy results obtained from testing on the open Model Zoo serve as reference metrics for evaluating ASIC hardware integration.

LightRAG is a repository hosting the code for LightRAG, a system that supports seamless integration of custom knowledge graphs, Oracle Database 23ai, Neo4J for storage, and multiple file types. It includes features like entity deletion, batch insert, incremental insert, and graph visualization. LightRAG provides an API server implementation for RESTful API access to RAG operations, allowing users to interact with it through HTTP requests. The repository also includes evaluation scripts, code for reproducing results, and a comprehensive code structure.

Compute Blade is a feature-rich carrier board designed for the Raspberry Pi Compute Module 4 and compatible alternatives, aimed at simplifying and automating the development and management of on-premise cluster environments. The solution streamlines complex processes, making cluster setup and management efficient and manageable for users, from homelabs to advanced AI clusters at scale.

TT-NN is a python & C++ Neural Network OP library. It provides a low-level programming model, TT-Metalium, enabling kernel development for Tenstorrent hardware.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.