EOLE is an open language modeling toolkit based on PyTorch. It aims to provide a research-friendly approach with a comprehensive yet compact and modular codebase for experimenting with various types of language models. The toolkit includes features such as versatile training and inference, dynamic data transforms, comprehensive large language model support, advanced quantization, efficient finetuning, flexible inference, and tensor parallelism. EOLE is a work in progress with ongoing enhancements in configuration management, command line entry points, reproducible recipes, core API simplification, and plans for further simplification, refactoring, inference server development, additional recipes, documentation enhancement, test coverage improvement, logging enhancements, and broader model support.

Kollektiv is a Retrieval-Augmented Generation (RAG) system designed to enable users to chat with their favorite documentation easily. It aims to provide LLMs with access to the most up-to-date knowledge, reducing inaccuracies and improving productivity. The system utilizes intelligent web crawling, advanced document processing, vector search, multi-query expansion, smart re-ranking, AI-powered responses, and dynamic system prompts. The technical stack includes Python/FastAPI for backend, Supabase, ChromaDB, and Redis for storage, OpenAI and Anthropic Claude 3.5 Sonnet for AI/ML, and Chainlit for UI. Kollektiv is licensed under a modified version of the Apache License 2.0, allowing free use for non-commercial purposes.

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.

Director is a framework to build video agents that can reason through complex video tasks like search, editing, compilation, generation, etc. It enables users to summarize videos, search for specific moments, create clips instantly, integrate GenAI projects and APIs, add overlays, generate thumbnails, and more. Built on VideoDB's 'video-as-data' infrastructure, Director is perfect for developers, creators, and teams looking to simplify media workflows and unlock new possibilities.

LocalAIVoiceChat is an experimental alpha software that enables real-time voice chat with a customizable AI personality and voice on your PC. It integrates Zephyr 7B language model with speech-to-text and text-to-speech libraries. The tool is designed for users interested in state-of-the-art voice solutions and provides an early version of a local real-time chatbot.

This repository contains the code and instructions needed to build a sophisticated answer engine that leverages the capabilities of Groq, Mistral AI's Mixtral, Langchain.JS, Brave Search, Serper API, and OpenAI. Designed to efficiently return sources, answers, images, videos, and follow-up questions based on user queries, this project is an ideal starting point for developers interested in natural language processing and search technologies.

MARBLE (Multi-Agent Coordination Backbone with LLM Engine) is a modular framework for developing, testing, and evaluating multi-agent systems leveraging Large Language Models. It provides a structured environment for agents to interact in simulated environments, utilizing cognitive abilities and communication mechanisms for collaborative or competitive tasks. The framework features modular design, multi-agent support, LLM integration, shared memory, flexible environments, metrics and evaluation, industrial coding standards, and Docker support.

APOLLO is a memory-efficient optimizer designed for large language model (LLM) pre-training and full-parameter fine-tuning. It offers SGD-like memory cost with AdamW-level performance. The optimizer integrates low-rank approximation and optimizer state redundancy reduction to achieve significant memory savings while maintaining or surpassing the performance of Adam(W). Key contributions include structured learning rate updates for LLM training, approximated channel-wise gradient scaling in a low-rank auxiliary space, and minimal-rank tensor-wise gradient scaling. APOLLO aims to optimize memory efficiency during training large language models.

Indexify is an open-source engine for building fast data pipelines for unstructured data (video, audio, images, and documents) using reusable extractors for embedding, transformation, and feature extraction. LLM Applications can query transformed content friendly to LLMs by semantic search and SQL queries. Indexify keeps vector databases and structured databases (PostgreSQL) updated by automatically invoking the pipelines as new data is ingested into the system from external data sources. **Why use Indexify** * Makes Unstructured Data **Queryable** with **SQL** and **Semantic Search** * **Real-Time** Extraction Engine to keep indexes **automatically** updated as new data is ingested. * Create **Extraction Graph** to describe **data transformation** and extraction of **embedding** and **structured extraction**. * **Incremental Extraction** and **Selective Deletion** when content is deleted or updated. * **Extractor SDK** allows adding new extraction capabilities, and many readily available extractors for **PDF**, **Image**, and **Video** indexing and extraction. * Works with **any LLM Framework** including **Langchain**, **DSPy**, etc. * Runs on your laptop during **prototyping** and also scales to **1000s of machines** on the cloud. * Works with many **Blob Stores**, **Vector Stores**, and **Structured Databases** * We have even **Open Sourced Automation** to deploy to Kubernetes in production.

Cosdata is a cutting-edge AI data platform designed to power the next generation search pipelines. It features immutability, version control, and excels in semantic search, structured knowledge graphs, hybrid search capabilities, real-time search at scale, and ML pipeline integration. The platform is customizable, scalable, efficient, enterprise-grade, easy to use, and can manage multi-modal data. It offers high performance, indexing, low latency, and high requests per second. Cosdata is designed to meet the demands of modern search applications, empowering businesses to harness the full potential of their data.

ClearML Server is a backend service infrastructure for ClearML, facilitating collaboration and experiment management. It includes a web app, RESTful API, and file server for storing images and models. Users can deploy ClearML Server using Docker, AWS EC2 AMI, or Kubernetes. The system design supports single IP or sub-domain configurations with specific open ports. ClearML-Agent Services container allows launching long-lasting jobs and various use cases like auto-scaler service, controllers, optimizer, and applications. Advanced functionality includes web login authentication and non-responsive experiments watchdog. Upgrading ClearML Server involves stopping containers, backing up data, downloading the latest docker-compose.yml file, configuring ClearML-Agent Services, and spinning up docker containers. Community support is available through ClearML FAQ, Stack Overflow, GitHub issues, and email contact.

VideoLingo is an all-in-one video translation and localization dubbing tool designed to generate Netflix-level high-quality subtitles. It aims to eliminate stiff machine translation, multiple lines of subtitles, and can even add high-quality dubbing, allowing knowledge from around the world to be shared across language barriers. Through an intuitive Streamlit web interface, the entire process from video link to embedded high-quality bilingual subtitles and even dubbing can be completed with just two clicks, easily creating Netflix-quality localized videos. Key features and functions include using yt-dlp to download videos from Youtube links, using WhisperX for word-level timeline subtitle recognition, using NLP and GPT for subtitle segmentation based on sentence meaning, summarizing intelligent term knowledge base with GPT for context-aware translation, three-step direct translation, reflection, and free translation to eliminate strange machine translation, checking single-line subtitle length and translation quality according to Netflix standards, using GPT-SoVITS for high-quality aligned dubbing, and integrating package for one-click startup and one-click output in streamlit.

AIBrix is an open-source initiative providing essential building blocks for scalable GenAI inference infrastructure. It delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored to enterprise needs. Key features include High-Density LoRA Management, LLM Gateway and Routing, LLM App-Tailored Autoscaler, Unified AI Runtime, Distributed Inference, Distributed KV Cache, Cost-efficient Heterogeneous Serving, and GPU Hardware Failure Detection.

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.

Motia is an AI agent framework designed for software engineers to create, test, and deploy production-ready AI agents quickly. It provides a code-first approach, allowing developers to write agent logic in familiar languages and visualize execution in real-time. With Motia, developers can focus on business logic rather than infrastructure, offering zero infrastructure headaches, multi-language support, composable steps, built-in observability, instant APIs, and full control over AI logic. Ideal for building sophisticated agents and intelligent automations, Motia's event-driven architecture and modular steps enable the creation of GenAI-powered workflows, decision-making systems, and data processing pipelines.

EOLE is an open language modeling toolkit based on PyTorch. It aims to provide a research-friendly approach with a comprehensive yet compact and modular codebase for experimenting with various types of language models. The toolkit includes features such as versatile training and inference, dynamic data transforms, comprehensive large language model support, advanced quantization, efficient finetuning, flexible inference, and tensor parallelism. EOLE is a work in progress with ongoing enhancements in configuration management, command line entry points, reproducible recipes, core API simplification, and plans for further simplification, refactoring, inference server development, additional recipes, documentation enhancement, test coverage improvement, logging enhancements, and broader model support.

The fsdp_qlora repository provides a script for training Large Language Models (LLMs) with Quantized LoRA and Fully Sharded Data Parallelism (FSDP). It integrates FSDP+QLoRA into the Axolotl platform and offers installation instructions for dependencies like llama-recipes, fastcore, and PyTorch. Users can finetune Llama-2 70B on Dual 24GB GPUs using the provided command. The script supports various training options including full params fine-tuning, LoRA fine-tuning, custom LoRA fine-tuning, quantized LoRA fine-tuning, and more. It also discusses low memory loading, mixed precision training, and comparisons to existing trainers. The repository addresses limitations and provides examples for training with different configurations, including BnB QLoRA and HQQ QLoRA. Additionally, it offers SLURM training support and instructions for adding support for a new model.

Pipeline is a Python library designed for constructing computational flows for AI/ML models. It supports both development and production environments, offering capabilities for inference, training, and finetuning. The library serves as an interface to Mystic, enabling the execution of pipelines at scale and on enterprise GPUs. Users can also utilize this SDK with Pipeline Core on a private hosted cluster. The syntax for defining AI/ML pipelines is reminiscent of sessions in Tensorflow v1 and Flows in Prefect.

IPEX-LLM is a low-bit LLM library on Intel XPU (Xeon/Core/Flex/Arc/PVC) that provides tutorials to help users understand and use the library to build LLM applications. The tutorials cover topics such as introduction to IPEX-LLM, environment setup, basic application development, Chinese language support, intermediate and advanced application development, GPU acceleration, and finetuning. Users can learn how to build chat applications, chatbots, speech recognition, and more using IPEX-LLM.

LLaSA_training is a repository focused on training models for speech synthesis using a large amount of open-source speech data. The repository provides instructions for finetuning models and offers pre-trained models for multilingual speech synthesis. It includes tools for training, data downloading, and data processing using specialized tokenizers for text and speech sequences. The repository also supports direct usage on Hugging Face platform with specific codecs and collections.

OpenVINO™ Model Server (OVMS) is a high-performance system for serving models. Implemented in C++ for scalability and optimized for deployment on Intel architectures, the model server uses the same architecture and API as TensorFlow Serving and KServe while applying OpenVINO for inference execution. Inference service is provided via gRPC or REST API, making deploying new algorithms and AI experiments easy.

TaskingAI brings Firebase's simplicity to **AI-native app development**. The platform enables the creation of GPTs-like multi-tenant applications using a wide range of LLMs from various providers. It features distinct, modular functions such as Inference, Retrieval, Assistant, and Tool, seamlessly integrated to enhance the development process. TaskingAI’s cohesive design ensures an efficient, intelligent, and user-friendly experience in AI application development.

MathCoder is a repository focused on enhancing mathematical reasoning by fine-tuning open-source language models to use code for modeling and deriving math equations. It introduces MathCodeInstruct dataset with solutions interleaving natural language, code, and execution results. The repository provides MathCoder models capable of generating code-based solutions for challenging math problems, achieving state-of-the-art scores on MATH and GSM8K datasets. It offers tools for model deployment, inference, and evaluation, along with a citation for referencing the work.

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.