The 'unilm' repository is a collection of tools, models, and architectures for Foundation Models and General AI, focusing on tasks such as NLP, MT, Speech, Document AI, and Multimodal AI. It includes various pre-trained models, such as UniLM, InfoXLM, DeltaLM, MiniLM, AdaLM, BEiT, LayoutLM, WavLM, VALL-E, and more, designed for tasks like language understanding, generation, translation, vision, speech, and multimodal processing. The repository also features toolkits like s2s-ft for sequence-to-sequence fine-tuning and Aggressive Decoding for efficient sequence-to-sequence decoding. Additionally, it offers applications like TrOCR for OCR, LayoutReader for reading order detection, and XLM-T for multilingual NMT.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

The 'summarize' tool is designed to transcribe and summarize videos from various sources using AI models. It helps users efficiently summarize lengthy videos, take notes, and extract key insights by providing timestamps, original transcripts, and support for auto-generated captions. Users can utilize different AI models via Groq, OpenAI, or custom local models to generate grammatically correct video transcripts and extract wisdom from video content. The tool simplifies the process of summarizing video content, making it easier to remember and reference important information.

KWeaver is an open-source cognitive intelligence development framework that provides data scientists, application developers, and domain experts with the ability for rapid development, comprehensive openness, and high-performance knowledge network generation and cognitive intelligence large model framework. It offers features such as automated and visual knowledge graph construction, visualization and analysis of knowledge graph data, knowledge graph integration, knowledge graph resource management, large model prompt engineering and debugging, and visual configuration for large model access.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

This repository provides a comprehensive guide to understanding and applying artificial intelligence (AI) theory, including basic knowledge, machine learning, deep learning, and natural language processing (BERT). It features detailed explanations, annotated code, and datasets to help users grasp the concepts and implement them in practice. The repository is continuously updated to ensure the latest information and best practices are covered.

LLMChat is an all-in-one AI chat interface that supports multiple language models, offers a plugin library for enhanced functionality, enables web search capabilities, allows customization of AI assistants, provides text-to-speech conversion, ensures secure local data storage, and facilitates data import/export. It also includes features like knowledge spaces, prompt library, personalization, and can be installed as a Progressive Web App (PWA). The tech stack includes Next.js, TypeScript, Pglite, LangChain, Zustand, React Query, Supabase, Tailwind CSS, Framer Motion, Shadcn, and Tiptap. The roadmap includes upcoming features like speech-to-text and knowledge spaces.

Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

Lidar AI Solution is a highly optimized repository for self-driving 3D lidar, providing solutions for sparse convolution, BEVFusion, CenterPoint, OSD, and Conversion. It includes CUDA and TensorRT implementations for various tasks such as 3D sparse convolution, BEVFusion, CenterPoint, PointPillars, V2XFusion, cuOSD, cuPCL, and YUV to RGB conversion. The repository offers easy-to-use solutions, high accuracy, low memory usage, and quantization options for different tasks related to self-driving technology.

XTuner is an efficient, flexible, and full-featured toolkit for fine-tuning large models. It supports various LLMs (InternLM, Mixtral-8x7B, Llama 2, ChatGLM, Qwen, Baichuan, ...), VLMs (LLaVA), and various training algorithms (QLoRA, LoRA, full-parameter fine-tune). XTuner also provides tools for chatting with pretrained / fine-tuned LLMs and deploying fine-tuned LLMs with any other framework, such as LMDeploy.

ComfyUI-fal-API is a repository containing custom nodes for using Flux models with fal API in ComfyUI. It provides nodes for image generation, video generation, language models, and vision language models. Users can easily install and configure the repository to access various nodes for different tasks such as generating images, creating videos, processing text, and understanding images. The repository also includes troubleshooting steps and is licensed under the Apache License 2.0.

AgentNeo is an advanced, open-source Agentic AI Application Observability, Monitoring, and Evaluation Framework designed to provide deep insights into AI agents, Large Language Model (LLM) calls, and tool interactions. It offers robust logging, visualization, and evaluation capabilities to help debug and optimize AI applications with ease. With features like tracing LLM calls, monitoring agents and tools, tracking interactions, detailed metrics collection, flexible data storage, simple instrumentation, interactive dashboard, project management, execution graph visualization, and evaluation tools, AgentNeo empowers users to build efficient, cost-effective, and high-quality AI-driven solutions.

Clash Royale Build-A-Bot is a project that allows users to build their own bot to play Clash Royale. It provides an advanced state generator that accurately returns detailed information using cutting-edge technologies. The project includes tutorials for setting up the environment, building a basic bot, and understanding state generation. It also offers updates such as replacing YOLOv5 with YOLOv8 unit model and enhancing performance features like placement and elixir management. The future roadmap includes plans to label more images of diverse cards, add a tracking layer for unit predictions, publish tutorials on Q-learning and imitation learning, release the YOLOv5 training notebook, implement chest opening and card upgrading features, and create a leaderboard for the best bots developed with this repository.

Anim v0.1.0 is an animation tool that allows users to convert videos to animations using mixamorig characters. It features FK animation editing, object selection, embedded Python support (only on Windows), and the ability to export to glTF and FBX formats. Users can also utilize Mediapipe to create animations. The tool is designed to assist users in creating animations with ease and flexibility.

The Google Cloud Document AI Samples repository contains code samples and Community Samples demonstrating how to analyze, classify, and search documents using Google Cloud Document AI. It includes various projects showcasing different functionalities such as integrating with Google Drive, processing documents using Python, content moderation with Dialogflow CX, fraud detection, language extraction, paper summarization, tax processing pipeline, and more. The repository also provides access to test document files stored in a publicly-accessible Google Cloud Storage Bucket. Additionally, there are codelabs available for optical character recognition (OCR), form parsing, specialized processors, and managing Document AI processors. Community samples, like the PDF Annotator Sample, are also included. Contributions are welcome, and users can seek help or report issues through the repository's issues page. Please note that this repository is not an officially supported Google product and is intended for demonstrative purposes only.

The StepChat Free service provides high-speed streaming output, multi-turn dialogue support, online search support, long document interpretation, and image parsing. It offers zero-configuration deployment, multi-token support, and automatic session trace cleaning. It is fully compatible with the ChatGPT interface. Additionally, it provides seven other free APIs for various services. The repository includes a disclaimer about using reverse APIs and encourages users to avoid commercial use to prevent service pressure on the official platform. It offers online testing links, showcases different demos, and provides deployment guides for Docker, Docker-compose, Render, Vercel, and native deployments. The repository also includes information on using multiple accounts, optimizing Nginx reverse proxy, and checking the liveliness of refresh tokens.

The 'unilm' repository is a collection of tools, models, and architectures for Foundation Models and General AI, focusing on tasks such as NLP, MT, Speech, Document AI, and Multimodal AI. It includes various pre-trained models, such as UniLM, InfoXLM, DeltaLM, MiniLM, AdaLM, BEiT, LayoutLM, WavLM, VALL-E, and more, designed for tasks like language understanding, generation, translation, vision, speech, and multimodal processing. The repository also features toolkits like s2s-ft for sequence-to-sequence fine-tuning and Aggressive Decoding for efficient sequence-to-sequence decoding. Additionally, it offers applications like TrOCR for OCR, LayoutReader for reading order detection, and XLM-T for multilingual NMT.

searchGPT is an open-source project that aims to build a search engine based on Large Language Model (LLM) technology to provide natural language answers. It supports web search with real-time results, file content search, and semantic search from sources like the Internet. The tool integrates LLM technologies such as OpenAI and GooseAI, and offers an easy-to-use frontend user interface. The project is designed to provide grounded answers by referencing real-time factual information, addressing the limitations of LLM's training data. Contributions, especially from frontend developers, are welcome under the MIT License.

This repository contains tutorials for using Large Language Models (LLMs) in the U.S. Department of Defense. The tutorials utilize open-source frameworks and LLMs, allowing users to run them in their own cloud environments. The repository is maintained by the Defense Digital Service and welcomes contributions from users.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

Eagle is a family of Vision-Centric High-Resolution Multimodal LLMs that enhance multimodal LLM perception using a mix of vision encoders and various input resolutions. The model features a channel-concatenation-based fusion for vision experts with different architectures and knowledge, supporting up to over 1K input resolution. It excels in resolution-sensitive tasks like optical character recognition and document understanding.

ERAG is an advanced system that combines lexical, semantic, text, and knowledge graph searches with conversation context to provide accurate and contextually relevant responses. This tool processes various document types, creates embeddings, builds knowledge graphs, and uses this information to answer user queries intelligently. It includes modules for interacting with web content, GitHub repositories, and performing exploratory data analysis using various language models.

Awesome Mamba is a curated collection of groundbreaking research papers and articles on Mamba Architecture, a pioneering framework in deep learning known for its selective state spaces and efficiency in processing complex data structures. The repository offers a comprehensive exploration of Mamba architecture through categorized research papers covering various domains like visual recognition, speech processing, remote sensing, video processing, activity recognition, image enhancement, medical imaging, reinforcement learning, natural language processing, 3D recognition, multi-modal understanding, time series analysis, graph neural networks, point cloud analysis, and tabular data handling.

The 'unilm' repository is a collection of tools, models, and architectures for Foundation Models and General AI, focusing on tasks such as NLP, MT, Speech, Document AI, and Multimodal AI. It includes various pre-trained models, such as UniLM, InfoXLM, DeltaLM, MiniLM, AdaLM, BEiT, LayoutLM, WavLM, VALL-E, and more, designed for tasks like language understanding, generation, translation, vision, speech, and multimodal processing. The repository also features toolkits like s2s-ft for sequence-to-sequence fine-tuning and Aggressive Decoding for efficient sequence-to-sequence decoding. Additionally, it offers applications like TrOCR for OCR, LayoutReader for reading order detection, and XLM-T for multilingual NMT.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

Awesome deep logic is a curated list of papers and resources focusing on integrating symbolic logic into deep neural networks. It includes surveys, tutorials, and research papers that explore the intersection of logic and deep learning. The repository aims to provide valuable insights and knowledge on how logic can be used to enhance reasoning, knowledge regularization, weak supervision, and explainability in neural networks.

Welcome to the ultimate hub for on-device Large Language Models (LLMs)! This repository is your go-to resource for all things related to LLMs designed for on-device deployment. Whether you're a seasoned researcher, an innovative developer, or an enthusiastic learner, this comprehensive collection of cutting-edge knowledge is your gateway to understanding, leveraging, and contributing to the exciting world of on-device LLMs.

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.