CoDrivingLLM is a machine learning model for predicting driving behavior based on sensor data collected from vehicles. It utilizes a Long Short-Term Memory (LSTM) neural network to analyze patterns in the data and make predictions about future driving actions. The model is trained on a large dataset of driving scenarios and can be used to improve driver assistance systems, enhance road safety, and optimize vehicle performance. CoDrivingLLM is designed to be easily integrated into existing automotive systems and can provide real-time feedback to drivers to help them make safer and more efficient driving decisions.

LightLLM is a lightweight library for linear and logistic regression models. It provides a simple and efficient way to train and deploy machine learning models for regression tasks. The library is designed to be easy to use and integrate into existing projects, making it suitable for both beginners and experienced data scientists. With LightLLM, users can quickly build and evaluate regression models using a variety of algorithms and hyperparameters. The library also supports feature engineering and model interpretation, allowing users to gain insights from their data and make informed decisions based on the model predictions.

ML-Retreat is a comprehensive machine learning library designed to simplify and streamline the process of building and deploying machine learning models. It provides a wide range of tools and utilities for data preprocessing, model training, evaluation, and deployment. With ML-Retreat, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to optimize their models. The library is built with a focus on scalability, performance, and ease of use, making it suitable for both beginners and experienced machine learning practitioners.

Checkpoint-engine is a middleware tool designed for updating model weights in LLM inference engines efficiently. It provides implementations for both Broadcast and P2P weight update methods, orchestrating the transfer process and controlling the inference engine through ZeroMQ socket. The tool optimizes weight broadcast by arranging data transfer into stages and organizing transfers into a pipeline for performance. It supports flexible installation options and is tested with various models and device setups. Checkpoint-engine also allows reusing weights from existing instances and provides a patch for FP8 quantization in vLLM.

Model Mondays is a repository dedicated to providing a collection of machine learning models implemented in Python. It aims to serve as a resource for individuals looking to explore and experiment with various machine learning algorithms and techniques. The repository includes a wide range of models, from simple linear regression to complex deep learning architectures, along with detailed documentation and examples to facilitate learning and understanding. Whether you are a beginner looking to get started with machine learning or an experienced practitioner seeking reference implementations, Model Mondays offers a valuable repository of models to study and leverage in your projects.

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

AI Edge Quantizer is a tool designed for advanced developers to quantize converted LiteRT models. It aims to optimize performance on resource-demanding models by providing quantization recipes for edge device deployment. The tool supports dynamic quantization, weight-only quantization, and static quantization methods, allowing users to customize the quantization process for different hardware deployments. Users can specify quantization recipes to apply to source models, resulting in quantized LiteRT models ready for deployment. The tool also includes advanced features such as selective quantization and mixed precision schemes for fine-tuning quantization recipes.

Memori is a lightweight and user-friendly memory management tool for developers. It helps in tracking memory usage, detecting memory leaks, and optimizing memory allocation in software projects. With Memori, developers can easily monitor and analyze memory consumption to improve the performance and stability of their applications. The tool provides detailed insights into memory usage patterns and helps in identifying areas for optimization. Memori is designed to be easy to integrate into existing projects and offers a simple yet powerful interface for managing memory resources effectively.

GraphBit is an industry-grade agentic AI framework built for developers and AI teams that demand stability, scalability, and low resource usage. It is written in Rust for maximum performance and safety, delivering significantly lower CPU usage and memory footprint compared to leading alternatives. The framework is designed to run multi-agent workflows in parallel, persist memory across steps, recover from failures, and ensure 100% task success under load. With lightweight architecture, observability, and concurrency support, GraphBit is suitable for deployment in high-scale enterprise environments and low-resource edge scenarios.

Nexent is a powerful tool for analyzing and visualizing network traffic data. It provides comprehensive insights into network behavior, helping users to identify patterns, anomalies, and potential security threats. With its user-friendly interface and advanced features, Nexent is suitable for network administrators, cybersecurity professionals, and anyone looking to gain a deeper understanding of their network infrastructure.

Nmed2024 is a GitHub repository that contains code for a neural network model designed for medical image analysis. The repository includes scripts for training the model, as well as pre-trained weights for quick deployment. The model is specifically tailored for detecting abnormalities in medical images, such as tumors or fractures. It utilizes deep learning techniques to achieve high accuracy and can be easily integrated into existing medical imaging systems. Researchers and developers in the healthcare industry can leverage this tool to enhance the efficiency and accuracy of medical image analysis tasks.

Earth2Studio is a Python-based package designed to enable users to quickly get started with AI weather and climate models. It provides access to pre-trained models, diagnostic tools, data sources, IO utilities, perturbation methods, and sample workflows for building custom weather prediction workflows. The package aims to empower users to explore AI-driven meteorology through modular components and seamless integration with other Nvidia packages like Modulus.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

This repository contains a collection of AI algorithms and models for various machine learning tasks. It provides implementations of popular algorithms such as neural networks, decision trees, and support vector machines. The code is well-documented and easy to understand, making it suitable for both beginners and experienced developers. The repository also includes example datasets and tutorials to help users get started with building and training AI models. Whether you are a student learning about AI or a professional working on machine learning projects, this repository can be a valuable resource for your development journey.

DeepFlow is an open-source project that provides deep observability for complex cloud-native and AI applications. It offers Zero Code data collection with eBPF for metrics, distributed tracing, request logs, and function profiling. DeepFlow is integrated with SmartEncoding to achieve Full Stack correlation and efficient access to all observability data. With DeepFlow, cloud-native and AI applications automatically gain deep observability, removing the burden of developers continually instrumenting code and providing monitoring and diagnostic capabilities covering everything from code to infrastructure for DevOps/SRE teams.

Airbrussh is a Capistrano plugin that enhances the output of Capistrano's deploy command. It provides a more detailed and structured view of the deployment process, including color-coded output, timestamps, and improved formatting. Airbrussh aims to make the deployment logs easier to read and understand, helping developers troubleshoot issues and monitor deployments more effectively. It is a useful tool for teams working with Capistrano to streamline their deployment workflows and improve visibility into the deployment process.

CoDrivingLLM is a machine learning model for predicting driving behavior based on sensor data collected from vehicles. It utilizes a Long Short-Term Memory (LSTM) neural network to analyze patterns in the data and make predictions about future driving actions. The model is trained on a large dataset of driving scenarios and can be used to improve driver assistance systems, enhance road safety, and optimize vehicle performance. CoDrivingLLM is designed to be easily integrated into existing automotive systems and can provide real-time feedback to drivers to help them make safer and more efficient driving decisions.

TigerBot is a cutting-edge foundation for your very own LLM, providing a world-class large model for innovative Chinese-style contributions. It offers various upgrades and features, such as search mode enhancements, support for large context lengths, and the ability to play text-based games. TigerBot is suitable for prompt-based game engine development, interactive game design, and real-time feedback for playable games.

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.