Llama Coder is a self-hosted Github Copilot replacement for VS Code that provides autocomplete using Ollama and Codellama. It works best with Mac M1/M2/M3 or RTX 4090, offering features like fast performance, no telemetry or tracking, and compatibility with any coding language. Users can install Ollama locally or on a dedicated machine for remote usage. The tool supports different models like stable-code and codellama with varying RAM/VRAM requirements, allowing users to optimize performance based on their hardware. Troubleshooting tips and a changelog are also provided for user convenience.

RAI is a framework designed to bring general multi-agent system capabilities to robots, enhancing human interactivity, flexibility in problem-solving, and out-of-the-box AI features. It supports multi-modalities, incorporates an advanced database for agent memory, provides ROS 2-oriented tooling, and offers a comprehensive task/mission orchestrator. The framework includes features such as voice interaction, customizable robot identity, camera sensor access, reasoning through ROS logs, and integration with LangChain for AI tools. RAI aims to support various AI vendors, improve human-robot interaction, provide an SDK for developers, and offer a user interface for configuration.

Refly.AI is an open-source AI-native creation engine that empowers users to transform ideas into production-ready content. It features a free-form canvas interface with multi-threaded conversations, knowledge base integration, contextual memory, intelligent search, WYSIWYG AI editor, and more. Users can leverage AI-powered capabilities, context memory, knowledge base integration, quotes, and AI document editing to enhance their content creation process. Refly offers both cloud and self-hosting options, making it suitable for individuals, enterprises, and organizations. The tool is designed to facilitate human-AI collaboration and streamline content creation workflows.

Torchtune is a PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. It provides native-PyTorch implementations of popular LLMs using composable and modular building blocks, easy-to-use and hackable training recipes for popular fine-tuning techniques, YAML configs for easily configuring training, evaluation, quantization, or inference recipes, and built-in support for many popular dataset formats and prompt templates to help you quickly get started with training.

KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy.

OmAgent is an open-source agent framework designed to streamline the development of on-device multimodal agents. It enables agents to empower various hardware devices, integrates speed-optimized SOTA multimodal models, provides SOTA multimodal agent algorithms, and focuses on optimizing the end-to-end computing pipeline for real-time user interaction experience. Key features include easy connection to diverse devices, scalability, flexibility, and workflow orchestration. The architecture emphasizes graph-based workflow orchestration, native multimodality, and device-centricity, allowing developers to create bespoke intelligent agent programs.

Dify is an open-source LLM app development platform that combines AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more. It allows users to quickly go from prototype to production. Key features include: 1. Workflow: Build and test powerful AI workflows on a visual canvas. 2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions. 3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features. 4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval. 5. Agent capabilities: Define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools. 6. LLMOps: Monitor and analyze application logs and performance over time. 7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs for easy integration into your own business logic.

PowerInfer is a high-speed Large Language Model (LLM) inference engine designed for local deployment on consumer-grade hardware, leveraging activation locality to optimize efficiency. It features a locality-centric design, hybrid CPU/GPU utilization, easy integration with popular ReLU-sparse models, and support for various platforms. PowerInfer achieves high speed with lower resource demands and is flexible for easy deployment and compatibility with existing models like Falcon-40B, Llama2 family, ProSparse Llama2 family, and Bamboo-7B.

PURE (Process-sUpervised Reinforcement lEarning) is a framework that trains a Process Reward Model (PRM) on a dataset and fine-tunes a language model to achieve state-of-the-art mathematical reasoning capabilities. It uses a novel credit assignment method to calculate return and supports multiple reward types. The final model outperforms existing methods with minimal RL data or compute resources, achieving high accuracy on various benchmarks. The tool addresses reward hacking issues and aims to enhance long-range decision-making and reasoning tasks using large language models.

ExLlamaV2 is an inference library designed for running local LLMs on modern consumer GPUs. The library supports paged attention via Flash Attention 2.5.7+, offers a new dynamic generator with features like dynamic batching, smart prompt caching, and K/V cache deduplication. It also provides an API for local or remote inference using TabbyAPI, with extended features like HF model downloading and support for HF Jinja2 chat templates. ExLlamaV2 aims to optimize performance and speed across different GPU models, with potential future optimizations and variations in speeds. The tool can be integrated with TabbyAPI for OpenAI-style web API compatibility and supports a standalone web UI called ExUI for single-user interaction with chat and notebook modes. ExLlamaV2 also offers support for text-generation-webui and lollms-webui through specific loaders and bindings.

Airclap is a user-friendly, cross-platform, ultra-fast, and beautifully designed file transfer tool that allows you to send any file to any device without requiring an internet connection. With its wireless transfer and nearby sharing capabilities, you can easily share files between devices on different platforms, including Mac, iOS, Windows, and Android. Airclap's modern user interface provides a simplified and intuitive transfer experience, with clear and real-time feedback. It also offers ultra-fast transfer speeds, maximum transfer speed available on the local network, and a highly stable transmission process. Additionally, Airclap provides abundant shortcuts and a clear file list with preview thumbnails for easy file management.

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

This open-source course teaches how to build an advanced RAG and LLM system using LLMOps and ML systems best practices. It helps you create an AI assistant that leverages your personal knowledge base to answer questions, summarize documents, and provide insights. The course covers topics such as LLM system architecture, pipeline orchestration, large-scale web crawling, model fine-tuning, and advanced RAG features. It is suitable for ML/AI engineers and data/software engineers & data scientists looking to level up to production AI systems. The course is free, with minimal costs for tools like OpenAI's API and Hugging Face's Dedicated Endpoints. Participants will build two separate Python applications for offline ML pipelines and online inference pipeline.

AgentLab is an open, easy-to-use, and extensible framework designed to accelerate web agent research. It provides features for developing and evaluating agents on various benchmarks supported by BrowserGym. The framework allows for large-scale parallel agent experiments using ray, building blocks for creating agents over BrowserGym, and a unified LLM API for OpenRouter, OpenAI, Azure, or self-hosted using TGI. AgentLab also offers reproducibility features, a unified LeaderBoard, and supports multiple benchmarks like WebArena, WorkArena, WebLinx, VisualWebArena, AssistantBench, GAIA, Mind2Web-live, and MiniWoB.

Ludwig is a declarative deep learning framework designed for scale and efficiency. It is a low-code framework that allows users to build custom AI models like LLMs and other deep neural networks with ease. Ludwig offers features such as optimized scale and efficiency, expert level control, modularity, and extensibility. It is engineered for production with prebuilt Docker containers, support for running with Ray on Kubernetes, and the ability to export models to Torchscript and Triton. Ludwig is hosted by the Linux Foundation AI & Data.

Llama Coder is a self-hosted Github Copilot replacement for VS Code that provides autocomplete using Ollama and Codellama. It works best with Mac M1/M2/M3 or RTX 4090, offering features like fast performance, no telemetry or tracking, and compatibility with any coding language. Users can install Ollama locally or on a dedicated machine for remote usage. The tool supports different models like stable-code and codellama with varying RAM/VRAM requirements, allowing users to optimize performance based on their hardware. Troubleshooting tips and a changelog are also provided for user convenience.

PearAI is an AI-powered code editor designed to enhance development by reducing the amount of coding required. It is a fork of VSCode and the main functionality lies within the 'extension/pearai' submodule. Users can contribute to the project by fixing issues, submitting bugs and feature requests, reviewing source code changes, and improving documentation. The tool aims to streamline the coding process and provide an efficient environment for developers to work in.

llm.nvim is a neovim plugin designed for LLM-assisted programming. It provides a no-frills approach to integrating language model assistance into the coding workflow. Users can configure the plugin to interact with various AI services such as GROQ, OpenAI, and Anthropics. The plugin offers functions to trigger the LLM assistant, create new prompt files, and customize key bindings for seamless interaction. With a focus on simplicity and efficiency, llm.nvim aims to enhance the coding experience by leveraging AI capabilities within the neovim environment.

AI Auto Free is a comprehensive automation tool that enables unlimited usage of AI-powered IDEs like Cursor and Windsurf. It offers cross-platform support and multiple language capabilities. The tool automates tasks such as account creation and email verification, overcoming trial limits and unauthorized requests. Users can use it for educational and research purposes to enhance their coding experience with AI.

The Mattermost AI Copilot Plugin is an extension that adds functionality for local and third-party LLMs within Mattermost v9.6 and above. It is currently experimental and allows users to interact with AI models seamlessly. The plugin enhances the user experience by providing AI-powered assistance and features for communication and collaboration within the Mattermost platform.

The Awesome ChatGPT Chinese Guide project aims to help Chinese users understand and use ChatGPT. It collects various free and paid ChatGPT resources, as well as methods to communicate more effectively with ChatGPT in Chinese. The repository contains a rich collection of ChatGPT tools, applications, and examples.

Zsh Codex is a ZSH plugin that enables AI-powered code completion in the command line. It supports both OpenAI's Codex and Google's Generative AI (Gemini), providing advanced language model capabilities for coding tasks directly in the terminal. Users can easily install the plugin and configure it to enhance their coding experience with AI assistance.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

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.