Model Hub V2 is a one-stop platform for model fine-tuning, deployment, and debugging without code, providing users with a visual interface to quickly validate the effects of fine-tuning various open-source models, facilitating rapid experimentation and decision-making, and lowering the threshold for users to fine-tune large models. For detailed instructions, please refer to the Feishu documentation.

EduChat is a large-scale language model-based chatbot system designed for intelligent education by the EduNLP team at East China Normal University. The project focuses on developing a dialogue-based language model for the education vertical domain, integrating diverse education vertical domain data, and providing functions such as automatic question generation, homework correction, emotional support, course guidance, and college entrance examination consultation. The tool aims to serve teachers, students, and parents to achieve personalized, fair, and warm intelligent education.

Browser4 is a lightning-fast, coroutine-safe browser designed for AI integration with large language models. It offers ultra-fast automation, deep web understanding, and powerful data extraction APIs. Users can automate the browser, extract data at scale, and perform tasks like summarizing products, extracting product details, and finding specific links. The tool is developer-friendly, supports AI-powered automation, and provides advanced features like X-SQL for precise data extraction. It also offers RPA capabilities, browser control, and complex data extraction with X-SQL. Browser4 is suitable for web scraping, data extraction, automation, and AI integration tasks.

DeepMCPAgent is a model-agnostic tool that enables the creation of LangChain/LangGraph agents powered by MCP tools over HTTP/SSE. It allows for dynamic discovery of tools, connection to remote MCP servers, and integration with any LangChain chat model instance. The tool provides a deep agent loop for enhanced functionality and supports typed tool arguments for validated calls. DeepMCPAgent emphasizes the importance of MCP-first approach, where agents dynamically discover and call tools rather than hardcoding them.

EvalPlus is a rigorous evaluation framework for LLM4Code, providing HumanEval+ and MBPP+ tests to evaluate large language models on code generation tasks. It offers precise evaluation and ranking, coding rigorousness analysis, and pre-generated code samples. Users can use EvalPlus to generate code solutions, post-process code, and evaluate code quality. The tool includes tools for code generation and test input generation using various backends.

Herc.ai is a powerful library for interacting with the Herc.ai API. It offers free access to users and supports all languages. Users can benefit from Herc.ai's features unlimitedly with a one-time subscription and API key. The tool provides functionalities for question answering and text-to-image generation, with support for various models and customization options. Herc.ai can be easily integrated into CLI, CommonJS, TypeScript, and supports beta models for advanced usage. Developed by FiveSoBes and Luppux Development.

Metaso AI Free service supports high-speed streaming output, secret tower AI super network search (full network or academic as well as concise, in-depth, research three modes), zero-configuration deployment, multi-token support. Fully compatible with ChatGPT interface. It also has seven other free APIs available for use. The tool provides various deployment options such as Docker, Docker-compose, Render, Vercel, and native deployment. Users can access the tool for chat completions and token live checks. Note: Reverse API is unstable, it is recommended to use the official Metaso AI website to avoid the risk of banning. This project is for research and learning purposes only, not for commercial use.

CareGPT is a medical large language model (LLM) that explores medical data, training, and deployment related research work. It integrates resources, open-source models, rich data, and efficient deployment methods. It supports various medical tasks, including patient diagnosis, medical dialogue, and medical knowledge integration. The model has been fine-tuned on diverse medical datasets to enhance its performance in the healthcare domain.

The 'meet-libai' project aims to promote and popularize the cultural heritage of the Chinese poet Li Bai by constructing a knowledge graph of Li Bai and training a professional AI intelligent body using large models. The project includes features such as data preprocessing, knowledge graph construction, question-answering system development, and visualization exploration of the graph structure. It also provides code implementations for large models and RAG retrieval enhancement.

Simba is an open source, portable Knowledge Management System (KMS) designed to seamlessly integrate with any Retrieval-Augmented Generation (RAG) system. It features a modern UI and modular architecture, allowing developers to focus on building advanced AI solutions without the complexities of knowledge management. Simba offers a user-friendly interface to visualize and modify document chunks, supports various vector stores and embedding models, and simplifies knowledge management for developers. It is community-driven, extensible, and aims to enhance AI functionality by providing a seamless integration with RAG-based systems.

ChatPilot is a chat agent tool that enables AgentChat conversations, supports Google search, URL conversation (RAG), and code interpreter functionality, replicates Kimi Chat (file, drag and drop; URL, send out), and supports OpenAI/Azure API. It is based on LangChain and implements ReAct and OpenAI Function Call for agent Q&A dialogue. The tool supports various automatic tools such as online search using Google Search API, URL parsing tool, Python code interpreter, and enhanced RAG file Q&A with query rewriting support. It also allows front-end and back-end service separation using Svelte and FastAPI, respectively. Additionally, it supports voice input/output, image generation, user management, permission control, and chat record import/export.

Aiotieba is an asynchronous Python library for interacting with the Tieba API. It provides a comprehensive set of features for working with Tieba, including support for authentication, thread and post management, and image and file uploading. Aiotieba is well-documented and easy to use, making it a great choice for developers who want to build applications that interact with Tieba.

FIT Framework is a Java enterprise AI development framework that provides a multi-language function engine (FIT), a flow orchestration engine (WaterFlow), and a Java ecosystem alternative solution (FEL). It runs in native/Spring dual mode, supports plug-and-play and intelligent deployment, seamlessly unifying large models and business systems. FIT Core offers language-agnostic computation base with plugin hot-swapping and intelligent deployment. WaterFlow Engine breaks the dimensional barrier of BPM and reactive programming, enabling graphical orchestration and declarative API-driven logic composition. FEL revolutionizes LangChain for the Java ecosystem, encapsulating large models, knowledge bases, and toolchains to integrate AI capabilities into Java technology stack seamlessly. The framework emphasizes engineering practices with intelligent conventions to reduce boilerplate code and offers flexibility for deep customization in complex scenarios.

botgroup.chat is a multi-person AI chat application based on React and Cloudflare Pages for free one-click deployment. It supports multiple AI roles participating in conversations simultaneously, providing an interactive experience similar to group chat. The application features real-time streaming responses, customizable AI roles and personalities, group management functionality, AI role mute function, Markdown format support, mathematical formula display with KaTeX, aesthetically pleasing UI design, and responsive design for mobile devices.

Neogenesis System is an advanced AI decision-making framework that enables agents to 'think about how to think'. It implements a metacognitive approach with real-time learning, tool integration, and multi-LLM support, allowing AI to make expert-level decisions in complex environments. Key features include metacognitive intelligence, tool-enhanced decisions, real-time learning, aha-moment breakthroughs, experience accumulation, and multi-LLM support.

QFurina is a powerful and easily extensible Python QQ robot backend service that provides a range of automation and interactive features. It supports multiple messaging platforms and has a robust plugin system, allowing users to easily expand and customize functionality.

The Google Cloud Vertex AI sample repository contains notebooks and community content that demonstrate how to develop and manage ML workflows using Google Cloud Vertex AI.

The ByteIR Project is a ByteDance model compilation solution. ByteIR includes compiler, runtime, and frontends, and provides an end-to-end model compilation solution. Although all ByteIR components (compiler/runtime/frontends) are together to provide an end-to-end solution, and all under the same umbrella of this repository, each component technically can perform independently. The name, ByteIR, comes from a legacy purpose internally. The ByteIR project is NOT an IR spec definition project. Instead, in most scenarios, ByteIR directly uses several upstream MLIR dialects and Google Mhlo. Most of ByteIR compiler passes are compatible with the selected upstream MLIR dialects and Google Mhlo.

Effort is an example implementation of the bucketMul algorithm, which allows for real-time adjustment of the number of calculations performed during inference of an LLM model. At 50% effort, it performs as fast as regular matrix multiplications on Apple Silicon chips; at 25% effort, it is twice as fast while still retaining most of the quality. Additionally, users have the option to skip loading the least important weights.

Ort is an unofficial ONNX Runtime 1.17 wrapper for Rust based on the now inactive onnxruntime-rs. ONNX Runtime accelerates ML inference on both CPU and GPU.

Model Hub V2 is a one-stop platform for model fine-tuning, deployment, and debugging without code, providing users with a visual interface to quickly validate the effects of fine-tuning various open-source models, facilitating rapid experimentation and decision-making, and lowering the threshold for users to fine-tune large models. For detailed instructions, please refer to the Feishu documentation.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.