Data Prep Kit accelerates unstructured data preparation for LLM app developers. It allows developers to cleanse, transform, and enrich unstructured data for pre-training, fine-tuning, instruct-tuning LLMs, or building RAG applications. The kit provides modules for Python, Ray, and Spark runtimes, supporting Natural Language and Code data modalities. It offers a framework for custom transforms and uses Kubeflow Pipelines for workflow automation. Users can install the kit via PyPi and access a variety of transforms for data processing pipelines.

This repository is a curated list of open-source, actively maintained web applications that leverage large language models (LLMs) for various use cases, including chatbots, natural language interfaces, assistants, and question answering systems. The projects are evaluated based on key criteria such as licensing, maintenance status, complexity, and features, to help users select the most suitable starting point for their LLM-based applications. The repository welcomes contributions and encourages users to submit projects that meet the criteria or suggest improvements to the existing list.

This repository is a curated list of research papers that explore the integration of Large Language Model (LLM) technology with tabular data. It aims to provide a comprehensive resource for researchers and practitioners interested in this emerging field. The repository includes papers on a wide range of topics, including table-to-text generation, table question answering, and tabular data classification. It also includes a section on related datasets and resources.

NNtrainer is a software framework for training neural network models on devices with limited resources. It enables on-device fine-tuning of neural networks using user data for personalization. NNtrainer supports various machine learning algorithms and provides examples for tasks such as few-shot learning, ResNet, VGG, and product rating. It is optimized for embedded devices and utilizes CBLAS and CUBLAS for accelerated calculations. NNtrainer is open source and released under the Apache License version 2.0.

Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.

visionOS-examples is a repository containing accelerators for Spatial Computing. It includes examples such as Local Large Language Model, Chat Apple Vision Pro, WebSockets, Anchor To Head, Hand Tracking, Battery Life, Countdown, Plane Detection, Timer Vision, and PencilKit for visionOS. The repository showcases various functionalities and features for Apple Vision Pro, offering tools for developers to enhance their visionOS apps with capabilities like hand tracking, plane detection, and real-time cryptocurrency prices.

Unstract is a no-code platform that enables users to launch APIs and ETL pipelines to structure unstructured documents. With Unstract, users can go beyond co-pilots by enabling machine-to-machine automation. Unstract's Prompt Studio provides a simple, no-code approach to creating prompts for LLMs, vector databases, embedding models, and text extractors. Users can then configure Prompt Studio projects as API deployments or ETL pipelines to automate critical business processes that involve complex documents. Unstract supports a wide range of LLM providers, vector databases, embeddings, text extractors, ETL sources, and ETL destinations, providing users with the flexibility to choose the best tools for their needs.

TRACE is a temporal grounding video model that utilizes causal event modeling to capture videos' inherent structure. It presents a task-interleaved video LLM model tailored for sequential encoding/decoding of timestamps, salient scores, and textual captions. The project includes various model checkpoints for different stages and fine-tuning on specific datasets. It provides evaluation codes for different tasks like VTG, MVBench, and VideoMME. The repository also offers annotation files and links to raw videos preparation projects. Users can train the model on different tasks and evaluate the performance based on metrics like CIDER, METEOR, SODA_c, F1, mAP, Hit@1, etc. TRACE has been enhanced with trace-retrieval and trace-uni models, showing improved performance on dense video captioning and general video understanding tasks.

Chat Your Doc is an experimental project exploring various applications based on LLM technology. It goes beyond being just a chatbot project, focusing on researching LLM applications using tools like LangChain and LlamaIndex. The project delves into UX, computer vision, and offers a range of examples in the 'Lab Apps' section. It includes links to different apps, descriptions, launch commands, and demos, aiming to showcase the versatility and potential of LLM applications.

A natural language to SQL plugin powered by large language models, supporting seamless database connection for zero-code SQL queries. The plugin is designed to facilitate communication and learning among users. It supports MySQL database and various large models for natural language processing. Users can quickly install the plugin, authorize a database address, import the plugin, select a model, and perform natural language SQL queries.

Kangaroo is an AI-powered SQL client and admin tool for popular databases like SQLite, MySQL, PostgreSQL, etc. It supports various functionalities such as table design, query, model, sync, export/import, and more. The tool is designed to be comfortable, fun, and developer-friendly, with features like code intellisense and autocomplete. Kangaroo aims to provide a seamless experience for database management across different operating systems.

GPUPixel is a real-time, high-performance image and video filter library written in C++11 and based on OpenGL/ES. It incorporates a built-in beauty face filter that achieves commercial-grade beauty effects. The library is extremely easy to compile and integrate with a small size, supporting platforms including iOS, Android, Mac, Windows, and Linux. GPUPixel provides various filters like skin smoothing, whitening, face slimming, big eyes, lipstick, and blush. It supports input formats like YUV420P, RGBA, JPEG, PNG, and output formats like RGBA and YUV420P. The library's performance on devices like iPhone and Android is optimized, with low CPU usage and fast processing times. GPUPixel's lib size is compact, making it suitable for mobile and desktop applications.

Nx is a build system optimized for monorepos, featuring AI-powered architectural awareness and advanced CI capabilities. It provides faster task scheduling, caching, and more for existing workspaces. Nx Cloud enhances CI by offering remote caching, task distribution, automated e2e test splitting, and task flakiness detection. The tool aims to scale monorepos efficiently and improve developer productivity.

Camera Chess Web is a tool that allows you to use your phone camera to replace chess eBoards. With Camera Chess Web, you can broadcast your game to Lichess, play a game on Lichess, or digitize a chess game from a video or live stream. Camera Chess Web is free to download on Google Play.

PredictorLLM is an advanced trading agent framework that utilizes large language models to automate trading in financial markets. It includes a profiling module to establish agent characteristics, a layered memory module for retaining and prioritizing financial data, and a decision-making module to convert insights into trading strategies. The framework mimics professional traders' behavior, surpassing human limitations in data processing and continuously evolving to adapt to market conditions for superior investment outcomes.

This repository provides resources, demos, and recipes for working with Large Language Models (LLMs) on OpenShift using OpenShift AI or Open Data Hub. It includes instructions for deploying inference servers for LLMs, such as vLLM, Hugging Face TGI, Caikit-TGIS-Serving, and Ollama. Additionally, it offers guidance on deploying serving runtimes, such as vLLM Serving Runtime and Hugging Face Text Generation Inference, in the Single-Model Serving stack of Open Data Hub or OpenShift AI. The repository also covers vector databases that can be used as a Vector Store for Retrieval Augmented Generation (RAG) applications, including Milvus, PostgreSQL+pgvector, and Redis. Furthermore, it provides examples of inference and application usage, such as Caikit, Langchain, Langflow, and UI examples.

draive is an open-source Python library designed to simplify and accelerate the development of LLM-based applications. It offers abstract building blocks for connecting functionalities with large language models, flexible integration with various AI solutions, and a user-friendly framework for building scalable data processing pipelines. The library follows a function-oriented design, allowing users to represent complex programs as simple functions. It also provides tools for measuring and debugging functionalities, ensuring type safety and efficient asynchronous operations for modern Python apps.

Data Prep Kit accelerates unstructured data preparation for LLM app developers. It allows developers to cleanse, transform, and enrich unstructured data for pre-training, fine-tuning, instruct-tuning LLMs, or building RAG applications. The kit provides modules for Python, Ray, and Spark runtimes, supporting Natural Language and Code data modalities. It offers a framework for custom transforms and uses Kubeflow Pipelines for workflow automation. Users can install the kit via PyPi and access a variety of transforms for data processing pipelines.

DeepSeek is a new experimental architecture for a large language model (LLM) powered internet-scale retrieval engine. Unlike current research agents designed as answer engines, DeepSeek aims to process a vast amount of sources to collect a comprehensive list of entities and enrich them with additional relevant data. The end result is a table with retrieved entities and enriched columns, providing a comprehensive overview of the topic. DeepSeek utilizes both standard keyword search and neural search to find relevant content, and employs an LLM to extract specific entities and their associated contents. It also includes a smaller answer agent to enrich the retrieved data, ensuring thoroughness. DeepSeek has the potential to revolutionize research and information gathering by providing a comprehensive and structured way to access information from the vastness of the internet.

MindsDB is a platform for customizing AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data. MindsDB "enhances" SQL syntax with AI capabilities to make it accessible for developers worldwide. With MindsDB’s nearly 200 integrations, any developer can create AI customized for their purpose, faster and more securely. Their AI systems will constantly improve themselves — using companies’ own data, in real-time.

Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, Tensorflow, XGBoost, MPI, Paddle and others. Training Operator allows you to use Kubernetes workloads to effectively train your large models via Kubernetes Custom Resources APIs or using Training Operator Python SDK. > Note: Before v1.2 release, Kubeflow Training Operator only supports TFJob on Kubernetes. * For a complete reference of the custom resource definitions, please refer to the API Definition. * TensorFlow API Definition * PyTorch API Definition * Apache MXNet API Definition * XGBoost API Definition * MPI API Definition * PaddlePaddle API Definition * For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator * For details on its observability, please refer to the monitoring design doc.

HelixML is a private GenAI platform that allows users to deploy the best of open AI in their own data center or VPC while retaining complete data security and control. It includes support for fine-tuning models with drag-and-drop functionality. HelixML brings the best of open source AI to businesses in an ergonomic and scalable way, optimizing the tradeoff between GPU memory and latency.

NNtrainer is a software framework for training neural network models on devices with limited resources. It enables on-device fine-tuning of neural networks using user data for personalization. NNtrainer supports various machine learning algorithms and provides examples for tasks such as few-shot learning, ResNet, VGG, and product rating. It is optimized for embedded devices and utilizes CBLAS and CUBLAS for accelerated calculations. NNtrainer is open source and released under the Apache License version 2.0.

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.