GenerativeAI is a repository focused on experimentation with various tools and techniques in the field of generative artificial intelligence. It covers topics such as large language models, frameworks like Langchain and llamaindex, vector databases, RAG systems, evaluations, performance optimization, production, use cases, and more.

Sarvadnya is a repository focused on interfacing custom data using Large Language Models (LLMs) through Proof-of-Concepts (PoCs) like Retrieval Augmented Generation (RAG) and Fine-Tuning. It aims to enable domain adaptation for LLMs to answer on user-specific corpora. The repository also covers topics such as Indic-languages models, 3D World Simulations, Knowledge Graphs Generation, Signal Processing, Drones, UAV Image Processing, and Floor Plan Segmentation. It provides insights into building chatbots of various modalities, preparing videos, and creating content for different platforms like Medium, LinkedIn, and YouTube. The tech stacks involved range from enterprise solutions like Google Doc AI and Microsoft Azure Language AI Services to open-source tools like Langchain and HuggingFace.

Animal-Artificial Intelligence (Animal-AI) is an interdisciplinary research platform designed to understand human, animal, and artificial cognition. It supports AI research to unlock cognitive capabilities and explore the space of possible minds. The open-source project facilitates testing across animals, humans, and AI, providing a comprehensive AI environment with a library of 900 tasks. It offers compatibility with Windows, Linux, and macOS, supporting Python 3.6.x and above. The environment utilizes Unity3D Game Engine, Unity ML-Agents toolkit, and provides interactive elements for AI training scenarios.

Dolma is a dataset and toolkit for curating large datasets for (pre)-training ML models. The dataset consists of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. The toolkit provides high-performance, portable, and extensible tools for processing, tagging, and deduplicating documents. Key features of the toolkit include built-in taggers, fast deduplication, and cloud support.

This repository is a collection of LLM notebooks with tutorials and projects. It covers topics such as Transformers tutorials, LLM notebooks and their applications, tools and technologies of GenAI, courses in GenAI, and Generative AI blogs/articles. Contributions are welcome.

ChatWise is an offline tool that supports various AI models such as OpenAI, Anthropic, Google AI, Groq, and Ollama. It is multi-modal, allowing text-to-speech powered by OpenAI and ElevenLabs. The tool supports text files, PDFs, audio, and images across different models. ChatWise is currently available for macOS (Apple Silicon & Intel) with Windows support coming soon.

Bisheng is a leading open-source **large model application development platform** that empowers and accelerates the development and deployment of large model applications, helping users enter the next generation of application development with the best possible experience.

Talemate is a roleplay tool that allows users to interact with AI agents for dialogue, narration, summarization, direction, editing, world state management, character/scenario creation, text-to-speech, and visual generation. It supports multiple AI clients and APIs, offers long-term memory using ChromaDB, and provides tools for managing NPCs, AI-assisted character creation, and scenario creation. Users can customize prompts using Jinja2 templates and benefit from a modern, responsive UI. The tool also integrates with Runpod for enhanced functionality.

MyScaleDB is a SQL vector database designed for scalable AI applications, enabling developers to efficiently manage and process massive volumes of data using familiar SQL. It offers fast and efficient vector search, filtered search, and SQL-vector join queries. MyScaleDB is fully SQL-compatible and production-ready for AI applications, providing unmatched performance and scalability through cutting-edge OLAP architecture and advanced vector algorithms. Built on top of ClickHouse, it combines structured and vectorized data management for high accuracy and speed in filtered searches.

MyScaleDB is a SQL vector database optimized for AI applications, enabling developers to manage and process massive volumes of data efficiently. It offers fast and powerful vector search, filtered search, and SQL-vector join queries, making it fully SQL-compatible. MyScaleDB provides unmatched performance and scalability by leveraging cutting-edge OLAP database architecture and advanced vector algorithms. It is production-ready for AI applications, supporting structured data, text, vector, JSON, geospatial, and time-series data. MyScale Cloud offers fully-managed MyScaleDB with premium features on billion-scale data, making it cost-effective and simpler to use compared to specialized vector databases. Built on top of ClickHouse, MyScaleDB combines structured and vector search efficiently, ensuring high accuracy and performance in filtered search operations.

Trafilatura is a Python package and command-line tool for gathering text on the Web and simplifying the process of turning raw HTML into structured, meaningful data. It includes components for web crawling, downloads, scraping, and extraction of main texts, metadata, and comments. The tool aims to focus on actual content, avoid noise, and make sense of data and metadata. It is robust, fast, and widely used by companies and institutions. Trafilatura outperforms other libraries in text extraction benchmarks and offers various features like support for sitemaps, parallel processing, configurable extraction of key elements, multiple output formats, and optional add-ons. The tool is actively maintained with regular updates and comprehensive documentation.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

LLMsForTimeSeries is a repository that questions the usefulness of language models in time series forecasting. The work shows that simple baselines outperform most language model-based time series forecasting models. It includes ablation studies on LLM-based TSF methods and introduces the PAttn method, showcasing the performance of patching and attention structures in forecasting. The repository provides datasets, setup instructions, and scripts for running ablations on different datasets.

KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and large language models. It is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG supports logical reasoning, multi-hop fact Q&A, and integrates knowledge and chunk mutual indexing structure, conceptual semantic reasoning, schema-constrained knowledge construction, and logical form-guided hybrid reasoning and retrieval. The framework includes kg-builder for knowledge representation and kg-solver for logical symbol-guided hybrid solving and reasoning engine. KAG aims to enhance LLM service framework in professional domains by integrating logical and factual characteristics of KGs.

Quanta is a new kind of Content Management platform, with powerful features including: Wikis & micro-blogging, ChatGPT Question Answering, Document collaboration and publishing, PDF Generation, Secure messaging with (E2E Encryption), Video/audio recording & sharing, File sharing, Podcatcher (RSS Reader), and many other features related to managing hierarchical content.

Transformer Explainer is an interactive visualization tool to help users learn how Transformer-based models like GPT work. It allows users to experiment with text and observe how internal components of the Transformer predict next tokens in real time. The tool runs a live GPT-2 model in the browser, providing an educational experience on text-generative models.

GenerativeAI is a repository focused on experimentation with various tools and techniques in the field of generative artificial intelligence. It covers topics such as large language models, frameworks like Langchain and llamaindex, vector databases, RAG systems, evaluations, performance optimization, production, use cases, and more.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Promptfoo is a tool for testing and evaluating LLM output quality. With promptfoo, you can build reliable prompts, models, and RAGs with benchmarks specific to your use-case, speed up evaluations with caching, concurrency, and live reloading, score outputs automatically by defining metrics, use as a CLI, library, or in CI/CD, and use OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, or integrate custom API providers for any LLM API.

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

The Vertex AI SDK for Python is a library that provides a convenient way to use the Vertex AI API. It offers a high-level interface for creating and managing Vertex AI resources, such as datasets, models, and endpoints. The SDK also provides support for training and deploying custom models, as well as using AutoML models. With the Vertex AI SDK for Python, you can quickly and easily build and deploy machine learning models on Vertex AI.

ScandEval is a framework for evaluating pretrained language models on mono- or multilingual language tasks. It provides a unified interface for benchmarking models on a variety of tasks, including sentiment analysis, question answering, and machine translation. ScandEval is designed to be easy to use and extensible, making it a valuable tool for researchers and practitioners alike.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

Flower is a framework for building federated learning systems. It is designed to be customizable, extensible, framework-agnostic, and understandable. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand.

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.