The AirBnB Clone - The Console project is the first segment of the AirBnB project at Holberton School, aiming to cover fundamental concepts of higher level programming. The goal is to deploy a server as a simple copy of the AirBnB Website (HBnB). The project includes a command interpreter to manage objects for the AirBnB website, allowing users to create new objects, retrieve objects, perform operations on objects, update object attributes, and destroy objects. The project is interpreted/tested on Ubuntu 14.04 LTS using Python 3.4.3.

FastFit is a Python package designed for fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. It utilizes a novel approach integrating batch contrastive learning and token-level similarity score, significantly improving multi-class classification performance in speed and accuracy across various datasets. FastFit provides a convenient command-line tool for training text classification models with customizable parameters. It offers a 3-20x improvement in training speed, completing training in just a few seconds. Users can also train models with Python scripts and perform inference using pretrained models for text classification tasks.

LLM Scraper is a TypeScript library that allows you to convert any webpages into structured data using LLMs. It supports Local (GGUF), OpenAI, Groq chat models, and schemas defined with Zod. With full type-safety in TypeScript and based on the Playwright framework, it offers streaming when crawling multiple pages and supports four input modes: html, markdown, text, and image.

Python-tgpt is a Python package that enables seamless interaction with over 45 free LLM providers without requiring an API key. It also provides image generation capabilities. The name _python-tgpt_ draws inspiration from its parent project tgpt, which operates on Golang. Through this Python adaptation, users can effortlessly engage with a number of free LLMs available, fostering a smoother AI interaction experience.

AICSImageIO is a Python tool for Image Reading, Metadata Conversion, and Image Writing for Microscopy Images. It supports various file formats like OME-TIFF, TIFF, ND2, DV, CZI, LIF, PNG, GIF, and Bio-Formats. Users can read and write metadata and imaging data, work with different file systems like local paths, HTTP URLs, s3fs, and gcsfs. The tool provides functionalities for full image reading, delayed image reading, mosaic image reading, metadata reading, xarray coordinate plane attachment, cloud IO support, and saving to OME-TIFF. It also offers benchmarking and developer resources.

OpenedAI Speech is a free, private text-to-speech server compatible with the OpenAI audio/speech API. It offers custom voice cloning and supports various models like tts-1 and tts-1-hd. Users can map their own piper voices and create custom cloned voices. The server provides multilingual support with XTTS voices and allows fixing incorrect sounds with regex. Recent changes include bug fixes, improved error handling, and updates for multilingual support. Installation can be done via Docker or manual setup, with usage instructions provided. Custom voices can be created using Piper or Coqui XTTS v2, with guidelines for preparing audio files. The tool is suitable for tasks like generating speech from text, creating custom voices, and multilingual text-to-speech applications.

**OpenAI Chat API Workflow for Alfred** An Alfred 5 Workflow for using OpenAI Chat API to interact with GPT-3.5/GPT-4 🤖💬 It also allows image generation 🖼️, image understanding 👀, speech-to-text conversion 🎤, and text-to-speech synthesis 🔈 **Features:** * Execute all features using Alfred UI, selected text, or a dedicated web UI * Web UI is constructed by the workflow and runs locally on your Mac 💻 * API call is made directly between the workflow and OpenAI, ensuring your chat messages are not shared online with anyone other than OpenAI 🔒 * OpenAI does not use the data from the API Platform for training 🚫 * Export chat data to a simple JSON format external file 📄 * Continue the chat by importing the exported data later 🔄

LitServe is a high-throughput serving engine for deploying AI models at scale. It generates an API endpoint for a model, handles batching, streaming, autoscaling across CPU/GPUs, and more. Built for enterprise scale, it supports every framework like PyTorch, JAX, Tensorflow, and more. LitServe is designed to let users focus on model performance, not the serving boilerplate. It is like PyTorch Lightning for model serving but with broader framework support and scalability.

Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator support including CUDA GPU, ROCm GPU, TPU, IPU and other NPUs. It allocates and isolates the underlying computing resources for multi-tenant computation sessions on-demand or in batches with customizable job schedulers with its own orchestrator. All its functions are exposed as REST/GraphQL/WebSocket APIs.

The receipt-scanner repository is an AI-Powered Receipt and Invoice Scanner for Laravel that allows users to easily extract structured receipt data from images, PDFs, and emails within their Laravel application using OpenAI. It provides a light wrapper around OpenAI Chat and Completion endpoints, supports various input formats, and integrates with Textract for OCR functionality. Users can install the package via composer, publish configuration files, and use it to extract data from plain text, PDFs, images, Word documents, and web content. The scanned receipt data is parsed into a DTO structure with main classes like Receipt, Merchant, and LineItem.

VideoDB Python SDK allows you to interact with the VideoDB serverless database. Manage videos as intelligent data, not files. It's scalable, cost-efficient & optimized for AI applications and LLM integration. The SDK provides functionalities for uploading videos, viewing videos, streaming specific sections of videos, searching inside a video, searching inside multiple videos in a collection, adding subtitles to a video, generating thumbnails, and more. It also offers features like indexing videos by spoken words, semantic indexing, and future indexing options for scenes, faces, and specific domains like sports. The SDK aims to simplify video management and enhance AI applications with video data.

nano-GraphRAG is a simple, easy-to-hack implementation of GraphRAG that provides a smaller, faster, and cleaner version of the official implementation. It is about 800 lines of code, small yet scalable, asynchronous, and fully typed. The tool supports incremental insert, async methods, and various parameters for customization. Users can replace storage components and LLM functions as needed. It also allows for embedding function replacement and comes with pre-defined prompts for entity extraction and community reports. However, some features like covariates and global search implementation differ from the original GraphRAG. Future versions aim to address issues related to data source ID, community description truncation, and add new components.

AnglE is a library for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. It also serves as a general sentence embedding inference framework, allowing for inferring a variety of transformer-based sentence embeddings. The library supports various loss functions such as AnglE loss, Contrastive loss, CoSENT loss, and Espresso loss. It provides backbones like BERT-based models, LLM-based models, and Bi-directional LLM-based models for training on single or multi-GPU setups. AnglE has achieved significant performance on various benchmarks and offers official pretrained models for both BERT-based and LLM-based models.

LL3DA is a Large Language 3D Assistant that responds to both visual and textual interactions within complex 3D environments. It aims to help Large Multimodal Models (LMM) comprehend, reason, and plan in diverse 3D scenes by directly taking point cloud input and responding to textual instructions and visual prompts. LL3DA achieves remarkable results in 3D Dense Captioning and 3D Question Answering, surpassing various 3D vision-language models. The code is fully released, allowing users to train customized models and work with pre-trained weights. The tool supports training with different LLM backends and provides scripts for tuning and evaluating models on various tasks.

This project provides a local, OpenAI-compatible text-to-speech (TTS) API using `edge-tts`. It emulates the OpenAI TTS endpoint (`/v1/audio/speech`), enabling users to generate speech from text with various voice options and playback speeds, just like the OpenAI API. `edge-tts` uses Microsoft Edge's online text-to-speech service, making it completely free. The project supports multiple audio formats, adjustable playback speed, and voice selection options, providing a flexible and customizable TTS solution for users.

The HuggingFace Model Downloader is a utility tool for downloading models and datasets from the HuggingFace website. It offers multithreaded downloading for LFS files and ensures the integrity of downloaded models with SHA256 checksum verification. The tool provides features such as nested file downloading, filter downloads for specific LFS model files, support for HuggingFace Access Token, and configuration file support. It can be used as a library or a single binary for easy model downloading and inference in projects.

The AirBnB Clone - The Console project is the first segment of the AirBnB project at Holberton School, aiming to cover fundamental concepts of higher level programming. The goal is to deploy a server as a simple copy of the AirBnB Website (HBnB). The project includes a command interpreter to manage objects for the AirBnB website, allowing users to create new objects, retrieve objects, perform operations on objects, update object attributes, and destroy objects. The project is interpreted/tested on Ubuntu 14.04 LTS using Python 3.4.3.

db-ally is a library for creating natural language interfaces to data sources. It allows developers to outline specific use cases for a large language model (LLM) to handle, detailing the desired data format and the possible operations to fetch this data. db-ally effectively shields the complexity of the underlying data source from the model, presenting only the essential information needed for solving the specific use cases. Instead of generating arbitrary SQL, the model is asked to generate responses in a simplified query language.

Aiocache is an asyncio cache library that supports multiple backends such as memory, redis, and memcached. It provides a simple interface for functions like add, get, set, multi_get, multi_set, exists, increment, delete, clear, and raw. Users can easily install and use the library for caching data in Python applications. Aiocache allows for easy instantiation of caches and setup of cache aliases for reusing configurations. It also provides support for backends, serializers, and plugins to customize cache operations. The library offers detailed documentation and examples for different use cases and configurations.

Ask Astro is an open-source reference implementation of Andreessen Horowitz's LLM Application Architecture built by Astronomer. It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow® and Astronomer. Ask Astro includes Airflow DAGs for data ingestion, an API for business logic, a Slack bot, a public UI, and DAGs for processing user feedback. The tool is divided into data retrieval & embedding, prompt orchestration, and feedback loops.

The AI Tutor RAG System repository contains Jupyter notebooks supporting the RAG course, focusing on enhancing AI models with retrieval-based methods. It covers foundational and advanced concepts in retrieval-augmented generation, including data retrieval techniques, model integration with retrieval systems, and practical applications of RAG in real-world scenarios.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

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.

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

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.