HuixiangDou2 is a robustly optimized GraphRAG approach that integrates multiple open-source projects to improve performance in graph-based augmented generation. It conducts comparative experiments and achieves a significant score increase, leading to a GraphRAG implementation with recognized performance. The repository provides code improvements, dense retrieval for querying entities and relationships, real domain knowledge testing, and impact analysis on accuracy.

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.

Oat is a simple and efficient framework for running online LLM alignment algorithms. It implements a distributed Actor-Learner-Oracle architecture, with components optimized using state-of-the-art tools. Oat simplifies the experimental pipeline of LLM alignment by serving an Oracle online for preference data labeling and model evaluation. It provides a variety of oracles for simulating feedback and supports verifiable rewards. Oat's modular structure allows for easy inheritance and modification of classes, enabling rapid prototyping and experimentation with new algorithms. The framework implements cutting-edge online algorithms like PPO for math reasoning and various online exploration algorithms.

MME-RealWorld is a benchmark designed to address real-world applications with practical relevance, featuring 13,366 high-resolution images and 29,429 annotations across 43 tasks. It aims to provide substantial recognition challenges and overcome common barriers in existing Multimodal Large Language Model benchmarks, such as small data scale, restricted data quality, and insufficient task difficulty. The dataset offers advantages in data scale, data quality, task difficulty, and real-world utility compared to existing benchmarks. It also includes a Chinese version with additional images and QA pairs focused on Chinese scenarios.

PostgresML is a powerful Postgres extension that seamlessly combines data storage and machine learning inference within your database. It enables running machine learning and AI operations directly within PostgreSQL, leveraging GPU acceleration for faster computations, integrating state-of-the-art large language models, providing built-in functions for text processing, enabling efficient similarity search, offering diverse ML algorithms, ensuring high performance, scalability, and security, supporting a wide range of NLP tasks, and seamlessly integrating with existing PostgreSQL tools and client libraries.

CogVideo is an open-source repository that provides pretrained text-to-video models for generating videos based on input text. It includes models like CogVideoX-2B and CogVideo, offering powerful video generation capabilities. The repository offers tools for inference, fine-tuning, and model conversion, along with demos showcasing the model's capabilities through CLI, web UI, and online experiences. CogVideo aims to facilitate the creation of high-quality videos from textual descriptions, catering to a wide range of applications.

LangGraph.js is a library for building stateful, multi-actor applications with LLMs, offering benefits such as cycles, controllability, and persistence. It allows defining flows involving cycles, providing fine-grained control over application flow and state. Inspired by Pregel and Apache Beam, it includes features like loops, persistence, human-in-the-loop workflows, and streaming support. LangGraph integrates seamlessly with LangChain.js and LangSmith but can be used independently.

OREAL is a reinforcement learning framework designed for mathematical reasoning tasks, aiming to achieve optimal performance through outcome reward-based learning. The framework utilizes behavior cloning, reshaping rewards, and token-level reward models to address challenges in sparse rewards and partial correctness. OREAL has achieved significant results, with a 7B model reaching 94.0 pass@1 accuracy on MATH-500 and surpassing previous 32B models. The tool provides training tutorials and Hugging Face model repositories for easy access and implementation.

EDA GPT is an open-source data analysis companion that offers a comprehensive solution for structured and unstructured data analysis. It streamlines the data analysis process, empowering users to explore, visualize, and gain insights from their data. EDA GPT supports analyzing structured data in various formats like CSV, XLSX, and SQLite, generating graphs, and conducting in-depth analysis of unstructured data such as PDFs and images. It provides a user-friendly interface, powerful features, and capabilities like comparing performance with other tools, analyzing large language models, multimodal search, data cleaning, and editing. The tool is optimized for maximal parallel processing, searching internet and documents, and creating analysis reports from structured and unstructured data.

LLM-Zero-to-Hundred is a repository showcasing various applications of LLM chatbots and providing insights into training and fine-tuning Language Models. It includes projects like WebGPT, RAG-GPT, WebRAGQuery, LLM Full Finetuning, RAG-Master LLamaindex vs Langchain, open-source-RAG-GEMMA, and HUMAIN: Advanced Multimodal, Multitask Chatbot. The projects cover features like ChatGPT-like interaction, RAG capabilities, image generation and understanding, DuckDuckGo integration, summarization, text and voice interaction, and memory access. Tutorials include LLM Function Calling and Visualizing Text Vectorization. The projects have a general structure with folders for README, HELPER, .env, configs, data, src, images, and utils.

This repository contains a sophisticated deterministic graph-based solution for answering complex questions using a controllable autonomous agent. The solution is designed to ensure that answers are solely based on the provided data, avoiding hallucinations. It involves various steps such as PDF loading, text preprocessing, summarization, database creation, encoding, and utilizing large language models. The algorithm follows a detailed workflow involving planning, retrieval, answering, replanning, content distillation, and performance evaluation. Heuristics and techniques implemented focus on content encoding, anonymizing questions, task breakdown, content distillation, chain of thought answering, verification, and model performance evaluation.

AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.

Upsonic offers a cutting-edge enterprise-ready framework for orchestrating LLM calls, agents, and computer use to complete tasks cost-effectively. It provides reliable systems, scalability, and a task-oriented structure for real-world cases. Key features include production-ready scalability, task-centric design, MCP server support, tool-calling server, computer use integration, and easy addition of custom tools. The framework supports client-server architecture and allows seamless deployment on AWS, GCP, or locally using Docker.

PremSQL is an open-source library designed to help developers create secure, fully local Text-to-SQL solutions using small language models. It provides essential tools for building and deploying end-to-end Text-to-SQL pipelines with customizable components, ideal for secure, autonomous AI-powered data analysis. The library offers features like Local-First approach, Customizable Datasets, Robust Executors and Evaluators, Advanced Generators, Error Handling and Self-Correction, Fine-Tuning Support, and End-to-End Pipelines. Users can fine-tune models, generate SQL queries from natural language inputs, handle errors, and evaluate model performance against predefined metrics. PremSQL is extendible for customization and private data usage.

PeriDyno is a CUDA-based, highly parallel physics engine targeted at providing real-time simulation of physical environments for intelligent agents. It is designed to be easy to use and integrate into existing projects, and it provides a wide range of features for simulating a variety of physical phenomena. PeriDyno is open source and available under the Apache 2.0 license.

RetouchGPT is a novel framework designed for interactive face retouching using Large Language Models (LLMs). It leverages instruction-driven imperfection prediction and LLM-based embedding to guide the retouching process. The tool allows users to interactively modify imperfection features in face images, achieving high-fidelity retouching results. RetouchGPT outperforms existing methods by integrating textual and visual features to accurately identify imperfections and replace them with normal skin features.

HuixiangDou2 is a robustly optimized GraphRAG approach that integrates multiple open-source projects to improve performance in graph-based augmented generation. It conducts comparative experiments and achieves a significant score increase, leading to a GraphRAG implementation with recognized performance. The repository provides code improvements, dense retrieval for querying entities and relationships, real domain knowledge testing, and impact analysis on accuracy.

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.

MSCCL++ is a GPU-driven communication stack for scalable AI applications. It provides a highly efficient and customizable communication stack for distributed GPU applications. MSCCL++ redefines inter-GPU communication interfaces, delivering a highly efficient and customizable communication stack for distributed GPU applications. Its design is specifically tailored to accommodate diverse performance optimization scenarios often encountered in state-of-the-art AI applications. MSCCL++ provides communication abstractions at the lowest level close to hardware and at the highest level close to application API. The lowest level of abstraction is ultra light weight which enables a user to implement logics of data movement for a collective operation such as AllReduce inside a GPU kernel extremely efficiently without worrying about memory ordering of different ops. The modularity of MSCCL++ enables a user to construct the building blocks of MSCCL++ in a high level abstraction in Python and feed them to a CUDA kernel in order to facilitate the user's productivity. MSCCL++ provides fine-grained synchronous and asynchronous 0-copy 1-sided abstracts for communication primitives such as `put()`, `get()`, `signal()`, `flush()`, and `wait()`. The 1-sided abstractions allows a user to asynchronously `put()` their data on the remote GPU as soon as it is ready without requiring the remote side to issue any receive instruction. This enables users to easily implement flexible communication logics, such as overlapping communication with computation, or implementing customized collective communication algorithms without worrying about potential deadlocks. Additionally, the 0-copy capability enables MSCCL++ to directly transfer data between user's buffers without using intermediate internal buffers which saves GPU bandwidth and memory capacity. MSCCL++ provides consistent abstractions regardless of the location of the remote GPU (either on the local node or on a remote node) or the underlying link (either NVLink/xGMI or InfiniBand). This simplifies the code for inter-GPU communication, which is often complex due to memory ordering of GPU/CPU read/writes and therefore, is error-prone.

This repository contains tools and libraries for building AIR platforms, runtimes and compilers.

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AIMr is an AI aimbot tool written in Python that leverages modern technologies to achieve an undetected system with a pleasing appearance. It works on any game that uses human-shaped models. To optimize its performance, users should build OpenCV with CUDA. For Valorant, additional perks in the Discord and an Arduino Leonardo R3 are required.

AIKA (Artificial Intelligence for Knowledge Acquisition) is a new type of artificial neural network designed to mimic the behavior of a biological brain more closely and bridge the gap to classical AI. The network conceptually separates activations from neurons, creating two separate graphs to represent acquired knowledge and inferred information. It uses different types of neurons and synapses to propagate activation values, binding signals, causal relations, and training gradients. The network structure allows for flexible topology and supports the gradual population of neurons and synapses during training.

Nextpy is a cutting-edge software development framework optimized for AI-based code generation. It provides guardrails for defining AI system boundaries, structured outputs for prompt engineering, a powerful prompt engine for efficient processing, better AI generations with precise output control, modularity for multiplatform and extensible usage, developer-first approach for transferable knowledge, and containerized & scalable deployment options. It offers 4-10x faster performance compared to Streamlit apps, with a focus on cooperation within the open-source community and integration of key components from various projects.

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.