VideoTuna is a codebase for text-to-video applications that integrates multiple AI video generation models for text-to-video, image-to-video, and text-to-image generation. It provides comprehensive pipelines in video generation, including pre-training, continuous training, post-training, and fine-tuning. The models in VideoTuna include U-Net and DiT architectures for visual generation tasks, with upcoming releases of a new 3D video VAE and a controllable facial video generation model.

Rig is a Rust library designed for building scalable, modular, and user-friendly applications powered by large language models (LLMs). It provides full support for LLM completion and embedding workflows, offers simple yet powerful abstractions for LLM providers like OpenAI and Cohere, as well as vector stores such as MongoDB and in-memory storage. With Rig, users can easily integrate LLMs into their applications with minimal boilerplate code.

PromptClip is a tool that allows developers to create video clips using LLM prompts. Users can upload videos from various sources, prompt the video in natural language, use different LLM models, instantly watch the generated clips, finetune the clips, and add music or image overlays. The tool provides a seamless way to extract specific moments from videos based on user queries, making video editing and content creation more efficient and intuitive.

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB

QMedia is an open-source multimedia AI content search engine designed specifically for content creators. It provides rich information extraction methods for text, image, and short video content. The tool integrates unstructured text, image, and short video information to build a multimodal RAG content Q&A system. Users can efficiently search for image/text and short video materials, analyze content, provide content sources, and generate customized search results based on user interests and needs. QMedia supports local deployment for offline content search and Q&A for private data. The tool offers features like content cards display, multimodal content RAG search, and pure local multimodal models deployment. Users can deploy different types of models locally, manage language models, feature embedding models, image models, and video models. QMedia aims to spark new ideas for content creation and share AI content creation concepts in an open-source manner.

Auto-News is an automatic news aggregator tool that utilizes Large Language Models (LLM) to pull information from various sources such as Tweets, RSS feeds, YouTube videos, web articles, Reddit, and journal notes. The tool aims to help users efficiently read and filter content based on personal interests, providing a unified reading experience and organizing information effectively. It features feed aggregation with summarization, transcript generation for videos and articles, noise reduction, task organization, and deep dive topic exploration. The tool supports multiple LLM backends, offers weekly top-k aggregations, and can be deployed on Linux/MacOS using docker-compose or Kubernetes.

Aichat is an AI-powered CLI chat and copilot tool that seamlessly integrates with over 10 leading AI platforms, providing a powerful combination of chat-based interaction, context-aware conversations, and AI-assisted shell capabilities, all within a customizable and user-friendly environment.

SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system. The core features of SGLang include: - **A Flexible Front-End Language**: This allows for easy programming of LLM applications with multiple chained generation calls, advanced prompting techniques, control flow, multiple modalities, parallelism, and external interaction. - **A High-Performance Runtime with RadixAttention**: This feature significantly accelerates the execution of complex LLM programs by automatic KV cache reuse across multiple calls. It also supports other common techniques like continuous batching and tensor parallelism.

SuperAgentX is a lightweight open-source AI framework designed for multi-agent applications with Artificial General Intelligence (AGI) capabilities. It offers goal-oriented multi-agents with retry mechanisms, easy deployment through WebSocket, RESTful API, and IO console interfaces, streamlined architecture with no major dependencies, contextual memory using SQL + Vector databases, flexible LLM configuration supporting various Gen AI models, and extendable handlers for integration with diverse APIs and data sources. It aims to accelerate the development of AGI by providing a powerful platform for building autonomous AI agents capable of executing complex tasks with minimal human intervention.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

MaxKB is a knowledge base Q&A system based on the LLM large language model. MaxKB = Max Knowledge Base, which aims to become the most powerful brain of the enterprise.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

Polaris establishes a novel, industry‑certified standard to foster the development of impactful methods in AI-based drug discovery. This library is a Python client to interact with the Polaris Hub. It allows you to download Polaris datasets and benchmarks, evaluate a custom method against a Polaris benchmark, and create and upload new datasets and benchmarks.

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.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

VideoTuna is a codebase for text-to-video applications that integrates multiple AI video generation models for text-to-video, image-to-video, and text-to-image generation. It provides comprehensive pipelines in video generation, including pre-training, continuous training, post-training, and fine-tuning. The models in VideoTuna include U-Net and DiT architectures for visual generation tasks, with upcoming releases of a new 3D video VAE and a controllable facial video generation model.

Lunary is an open-source observability and prompt platform for Large Language Models (LLMs). It provides a suite of features to help AI developers take their applications into production, including analytics, monitoring, prompt templates, fine-tuning dataset creation, chat and feedback tracking, and evaluations. Lunary is designed to be usable with any model, not just OpenAI, and is easy to integrate and self-host.

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.