Browser AI is a TypeScript library that provides access to in-browser AI model providers with seamless fallback to server-side models. It offers different packages for Chrome/Edge built-in browser AI models, open-source models via WebLLM, and 🤗 Transformers.js models. The library simplifies the process of integrating AI models into web applications by handling the complexities of custom hooks, UI components, state management, and compatibility with server-side models.

The react-native-ai repository allows users to run Large Language Models (LLM) locally in a React Native app using the Universal MLC LLM Engine with compatibility for Vercel AI SDK. Please note that this project is experimental and not ready for production. The repository is licensed under MIT and was created with create-react-native-library.

libLLM is an open-source project designed for efficient inference of large language models (LLM) on personal computers and mobile devices. It is optimized to run smoothly on common devices, written in C++14 without external dependencies, and supports CUDA for accelerated inference. Users can build the tool for CPU only or with CUDA support, and run libLLM from the command line. Additionally, there are API examples available for Python and the tool can export Huggingface models.

ScaleLLM is a cutting-edge inference system engineered for large language models (LLMs), meticulously designed to meet the demands of production environments. It extends its support to a wide range of popular open-source models, including Llama3, Gemma, Bloom, GPT-NeoX, and more. ScaleLLM is currently undergoing active development. We are fully committed to consistently enhancing its efficiency while also incorporating additional features. Feel free to explore our **_Roadmap_** for more details. ## Key Features * High Efficiency: Excels in high-performance LLM inference, leveraging state-of-the-art techniques and technologies like Flash Attention, Paged Attention, Continuous batching, and more. * Tensor Parallelism: Utilizes tensor parallelism for efficient model execution. * OpenAI-compatible API: An efficient golang rest api server that compatible with OpenAI. * Huggingface models: Seamless integration with most popular HF models, supporting safetensors. * Customizable: Offers flexibility for customization to meet your specific needs, and provides an easy way to add new models. * Production Ready: Engineered with production environments in mind, ScaleLLM is equipped with robust system monitoring and management features to ensure a seamless deployment experience.

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. It is developed under the MIT License and powers inference behind Gradient.ai. The API allows users to deploy models from SentenceTransformers, offers fast inference backends utilizing various accelerators, dynamic batching for efficient processing, correct and tested implementation, and easy-to-use API built on FastAPI with Swagger documentation. Users can embed text, rerank documents, and perform text classification tasks using the tool. Infinity supports various models from Huggingface and provides flexibility in deployment via CLI, Docker, Python API, and cloud services like dstack. The tool is suitable for tasks like embedding, reranking, and text classification.

AnglE is a library for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. It also serves as a general sentence embedding inference framework, allowing for inferring a variety of transformer-based sentence embeddings. The library supports various loss functions such as AnglE loss, Contrastive loss, CoSENT loss, and Espresso loss. It provides backbones like BERT-based models, LLM-based models, and Bi-directional LLM-based models for training on single or multi-GPU setups. AnglE has achieved significant performance on various benchmarks and offers official pretrained models for both BERT-based and LLM-based models.

DSPy.rb is a Ruby framework for building reliable LLM applications using composable, type-safe modules. It enables developers to define typed signatures and compose them into pipelines, offering a more structured approach compared to traditional prompting. The framework embraces Ruby conventions and adds innovations like CodeAct agents and enhanced production instrumentation, resulting in scalable LLM applications that are robust and efficient. DSPy.rb is actively developed, with a focus on stability and real-world feedback through the 0.x series before reaching a stable v1.0 API.

LTEngine is a free and open-source local AI machine translation API written in Rust. It is self-hosted and compatible with LibreTranslate. LTEngine utilizes large language models (LLMs) via llama.cpp, offering high-quality translations that rival or surpass DeepL for certain languages. It supports various accelerators like CUDA, Metal, and Vulkan, with the largest model 'gemma3-27b' fitting on a single consumer RTX 3090. LTEngine is actively developed, with a roadmap outlining future enhancements and features.

Candle-vllm is an efficient and easy-to-use platform designed for inference and serving local LLMs, featuring an OpenAI compatible API server. It offers a highly extensible trait-based system for rapid implementation of new module pipelines, streaming support in generation, efficient management of key-value cache with PagedAttention, and continuous batching. The tool supports chat serving for various models and provides a seamless experience for users to interact with LLMs through different interfaces.

Acontext is a context data platform designed for production AI agents, offering unified storage, built-in context management, and observability features. It helps agents scale from local demos to production without the need to rebuild context infrastructure. The platform provides solutions for challenges like scattered context data, long-running agents requiring context management, and tracking states from multi-modal agents. Acontext offers core features such as context storage, session management, disk storage, agent skills management, and sandbox for code execution and analysis. Users can connect to Acontext, install SDKs, initialize clients, store and retrieve messages, perform context engineering, and utilize agent storage tools. The platform also supports building agents using end-to-end scripts in Python and Typescript, with various templates available. Acontext's architecture includes client layer, backend with API and core components, infrastructure with PostgreSQL, S3, Redis, and RabbitMQ, and a web dashboard. Join the Acontext community on Discord and follow updates on GitHub.

This repository contains the code for the paper 'Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs'. It provides tools for fine-tuning and evaluating Llama models, converting models between different frameworks, processing datasets, and performing specific model training tasks like Partial-RoPE Fine-Tuning and Multiple-Head Latent Attention Fine-Tuning. The repository also includes commands for model evaluation using Lighteval and LongBench, along with necessary environment setup instructions.

BrowserAI is a production-ready tool that allows users to run AI models directly in the browser, offering simplicity, speed, privacy, and open-source capabilities. It provides WebGPU acceleration for fast inference, zero server costs, offline capability, and developer-friendly features. Perfect for web developers, companies seeking privacy-conscious AI solutions, researchers experimenting with browser-based AI, and hobbyists exploring AI without infrastructure overhead. The tool supports various AI tasks like text generation, speech recognition, and text-to-speech, with pre-configured popular models ready to use. It offers a simple SDK with multiple engine support and seamless switching between MLC and Transformers engines.

RankLLM is a suite of prompt-decoders compatible with open source LLMs like Vicuna and Zephyr. It allows users to create custom ranking models for various NLP tasks, such as document reranking, question answering, and summarization. The tool offers a variety of features, including the ability to fine-tune models on custom datasets, use different retrieval methods, and control the context size and variable passages. RankLLM is easy to use and can be integrated into existing NLP pipelines.

LangServe Ollama is a tool that allows users to fine-tune Korean language models for local hosting, including RAG. Users can load HuggingFace gguf files, create model chains, and monitor GPU usage. The tool provides a seamless workflow for customizing and deploying language models in a local environment.

Ahnlich is a tool that provides multiple components for storing and searching similar vectors using linear or non-linear similarity algorithms. It includes 'ahnlich-db' for in-memory vector key value store, 'ahnlich-ai' for AI proxy communication, 'ahnlich-client-rs' for Rust client, and 'ahnlich-client-py' for Python client. The tool is not production-ready yet and is still in testing phase, allowing AI/ML engineers to issue queries using raw input such as images/text and features off-the-shelf models for indexing and querying.

Mistral Inference repository contains minimal code to run 7B, 8x7B, and 8x22B models. It provides model download links, installation instructions, and usage guidelines for running models via CLI or Python. The repository also includes information on guardrailing, model platforms, deployment, and references. Users can interact with models through commands like mistral-demo, mistral-chat, and mistral-common. Mistral AI models support function calling and chat interactions for tasks like testing models, chatting with models, and using Codestral as a coding assistant. The repository offers detailed documentation and links to blogs for further information.

Browser AI is a TypeScript library that provides access to in-browser AI model providers with seamless fallback to server-side models. It offers different packages for Chrome/Edge built-in browser AI models, open-source models via WebLLM, and 🤗 Transformers.js models. The library simplifies the process of integrating AI models into web applications by handling the complexities of custom hooks, UI components, state management, and compatibility with server-side models.

The Google Generative AI SDK for Dart enables developers to utilize cutting-edge Large Language Models (LLMs) for creating language applications. It provides access to the Gemini API for generating content using state-of-the-art models. Developers can integrate the SDK into their Dart or Flutter applications to leverage powerful AI capabilities. It is recommended to use the SDK for server-side API calls to ensure the security of API keys and protect against potential key exposure in mobile or web apps.

This repository contains an assistant proposal for the Semantic Kernel, allowing the usage of assistants without relying on OpenAI Assistant APIs. It runs locally planners and plugins for the assistants, providing scenarios like Assistant with Semantic Kernel plugins, Multi-Assistant conversation, and AutoGen conversation. The Semantic Kernel is a lightweight SDK enabling integration of AI Large Language Models with conventional programming languages, offering functions like semantic functions, native functions, and embeddings-based memory. Users can bring their own model for the assistants and host them locally. The repository includes installation instructions, usage examples, and information on creating new conversation threads with the assistant.

ezlocalai is an artificial intelligence server that simplifies running multimodal AI models locally. It handles model downloading and server configuration based on hardware specs. It offers OpenAI Style endpoints for integration, voice cloning, text-to-speech, voice-to-text, and offline image generation. Users can modify environment variables for customization. Supports NVIDIA GPU and CPU setups. Provides demo UI and workflow visualization for easy usage.

llmproxy is a reverse proxy for LLM API based on Cloudflare Worker, supporting platforms like OpenAI, Gemini, and Groq. The interface is compatible with the OpenAI API specification and can be directly accessed using the OpenAI SDK. It provides a convenient way to interact with various AI platforms through a unified API endpoint, enabling seamless integration and usage in different applications.

This repository contains a simple Python Flask App utilizing the Google AI Gemini API to explore multi-modal capabilities. It provides a basic UI and Flask backend for easy integration and testing. The app allows users to interact with the AI model through chat messages, making it a great starting point for developers interested in AI-powered applications.

KaibanJS is a JavaScript-native framework for building multi-agent AI systems. It enables users to create specialized AI agents with distinct roles and goals, manage tasks, and coordinate teams efficiently. The framework supports role-based agent design, tool integration, multiple LLMs support, robust state management, observability and monitoring features, and a real-time agentic Kanban board for visualizing AI workflows. KaibanJS aims to empower JavaScript developers with a user-friendly AI framework tailored for the JavaScript ecosystem, bridging the gap in the AI race for non-Python developers.

FFAIVideo is a lightweight node.js project that utilizes popular AI LLM to intelligently generate short videos. It supports multiple AI LLM models such as OpenAI, Moonshot, Azure, g4f, Google Gemini, etc. Users can input text to automatically synthesize exciting video content with subtitles, background music, and customizable settings. The project integrates Microsoft Edge's online text-to-speech service for voice options and uses Pexels website for video resources. Installation of FFmpeg is essential for smooth operation. Inspired by MoneyPrinterTurbo, MoneyPrinter, and MsEdgeTTS, FFAIVideo is designed for front-end developers with minimal dependencies and simple usage.

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.