YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. It is built atop QUIC Transport Protocol and Stateful Serverless architecture, making AI applications low-latency, reliable, secure, and easy. The framework focuses on providing low-latency, secure, stateful serverless functions that can be distributed geographically to bring AI inference closer to end users. It offers features such as low-latency communication, security with TLS v1.3, stateful serverless functions for faster GPU processing, geo-distributed architecture, and a faster-than-real-time codec called Y3. YoMo enables developers to create and deploy stateful serverless functions for AI inference in a distributed manner, ensuring quick responses to user queries from various locations worldwide.

Heygem is an open-source, affordable alternative to Heygen, offering a fully offline video synthesis tool for Windows systems. It enables precise appearance and voice cloning, allowing users to digitalize their image and drive virtual avatars through text and voice for video production. With core features like efficient video synthesis and multi-language support, Heygem ensures a user-friendly experience with fully offline operation and support for multiple models. The tool leverages advanced AI algorithms for voice cloning, automatic speech recognition, and computer vision technology to enhance the virtual avatar's performance and synchronization.

SWE-RL is the official codebase for the paper 'SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution'. It is the first approach to scale reinforcement learning based LLM reasoning for real-world software engineering, leveraging open-source software evolution data and rule-based rewards. The code provides prompt templates and the implementation of the reward function based on sequence similarity. Agentless Mini, a part of SWE-RL, builds on top of Agentless with improvements like fast async inference, code refactoring for scalability, and support for using multiple reproduction tests for reranking. The tool can be used for localization, repair, and reproduction test generation in software engineering tasks.

BentoML is an open-source model serving library for building performant and scalable AI applications with Python. It comes with everything you need for serving optimization, model packaging, and production deployment.

BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

Call Center AI is an AI-powered call center solution leveraging Azure and OpenAI GPT. It allows for AI agent-initiated phone calls or direct calls to the bot from a configured phone number. The bot is customizable for various industries like insurance, IT support, and customer service, with features such as accessing claim information, conversation history, language change, SMS sending, and more. The project is a proof of concept showcasing the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI for an automated call center solution.

Suno AI API is an open-source project that allows developers to integrate the music generation capabilities of Suno.ai into their own applications. The API provides a simple and convenient way to generate music, lyrics, and other audio content using Suno.ai's powerful AI models. With Suno AI API, developers can easily add music generation functionality to their apps, websites, and other projects.

FastAPI-MCP is a zero-configuration tool that automatically exposes FastAPI endpoints as Model Context Protocol (MCP) tools. It allows for direct integration with FastAPI apps, automatic discovery and conversion of endpoints to MCP tools, preservation of request and response schemas, documentation preservation similar to Swagger, and the ability to extend with custom MCP tools. Users can easily add an MCP server to their FastAPI application and customize the server creation and configuration. The tool supports connecting to the MCP server using SSE or mcp-proxy stdio for different MCP clients. FastAPI-MCP is developed and maintained by Tadata Inc.

Ichigo is a local real-time voice AI tool that uses an early fusion technique to extend a text-based LLM to have native 'listening' ability. It is an open research experiment with improved multiturn capabilities and the ability to refuse processing inaudible queries. The tool is designed for open data, open weight, on-device Siri-like functionality, inspired by Meta's Chameleon paper. Ichigo offers a web UI demo and Gradio web UI for users to interact with the tool. It has achieved enhanced MMLU scores, stronger context handling, advanced noise management, and improved multi-turn capabilities for a robust user experience.

llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.

Generate React components on-the-fly at runtime using AI. Register your components, and let Hydra choose when to show them in your App. Hydra development is still early, and patterns for different types of components and apps are still being developed. Join the discord to chat with the developers. Expects to be used in a NextJS project. Components that have function props do not work.

Trace is a new AutoDiff-like tool for training AI systems end-to-end with general feedback. It generalizes the back-propagation algorithm by capturing and propagating an AI system's execution trace. Implemented as a PyTorch-like Python library, users can write Python code directly and use Trace primitives to optimize certain parts, similar to training neural networks.

PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

LLM Context is a tool designed to assist developers in quickly injecting relevant content from code/text projects into Large Language Model chat interfaces. It leverages `.gitignore` patterns for smart file selection and offers a streamlined clipboard workflow using the command line. The tool also provides direct integration with Large Language Models through the Model Context Protocol (MCP). LLM Context is optimized for code repositories and collections of text/markdown/html documents, making it suitable for developers working on projects that fit within an LLM's context window. The tool is under active development and aims to enhance AI-assisted development workflows by harnessing the power of Large Language Models.

LLM-Pruner is a tool for structural pruning of large language models, allowing task-agnostic compression while retaining multi-task solving ability. It supports automatic structural pruning of various LLMs with minimal human effort. The tool is efficient, requiring only 3 minutes for pruning and 3 hours for post-training. Supported LLMs include Llama-3.1, Llama-3, Llama-2, LLaMA, BLOOM, Vicuna, and Baichuan. Updates include support for new LLMs like GQA and BLOOM, as well as fine-tuning results achieving high accuracy. The tool provides step-by-step instructions for pruning, post-training, and evaluation, along with a Gradio interface for text generation. Limitations include issues with generating repetitive or nonsensical tokens in compressed models and manual operations for certain models.

Giskard is an open-source Python library that automatically detects performance, bias & security issues in AI applications. The library covers LLM-based applications such as RAG agents, all the way to traditional ML models for tabular data.

YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. It is built atop QUIC Transport Protocol and Stateful Serverless architecture, making AI applications low-latency, reliable, secure, and easy. The framework focuses on providing low-latency, secure, stateful serverless functions that can be distributed geographically to bring AI inference closer to end users. It offers features such as low-latency communication, security with TLS v1.3, stateful serverless functions for faster GPU processing, geo-distributed architecture, and a faster-than-real-time codec called Y3. YoMo enables developers to create and deploy stateful serverless functions for AI inference in a distributed manner, ensuring quick responses to user queries from various locations worldwide.

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.