Repository History
351 repositories tagged with Python
Cheshire Cat AI Core: An AI Agent Microservice Framework
Cheshire Cat AI Core is an open-source framework designed for building custom AI agents as microservices. It offers an API-first approach, enabling easy integration of conversational layers into applications with WebSocket chat and a customizable REST API. Key features include built-in RAG with Qdrant, extensibility via plugins, function calling, and full Dockerization for straightforward deployment.
Verifiers: Environments for LLM Reinforcement Learning and Evaluation
Verifiers is a Python library by Prime Intellect AI for building environments to train and evaluate Large Language Models (LLMs). It enables the creation of custom environments with datasets, model harnesses, and reward functions, supporting reinforcement learning, capability evaluation, and synthetic data generation. This library is tightly integrated with the Prime Intellect ecosystem, including their Environments Hub and training framework.
gaussian-splatting: Real-Time 3D Radiance Field Rendering
gaussian-splatting is the original reference implementation for real-time radiance field rendering. This repository introduces a novel approach using 3D Gaussians for high-quality, real-time novel-view synthesis at 1080p resolution, offering significant advancements in computer graphics and vision. Developed by GRAPHDECO Inria, it provides a robust framework for 3D scene reconstruction and interactive visualization.
LLMSanitize: An Open-Source Library for Contamination Detection in NLP and LLM Datasets
LLMSanitize is an open-source Python library designed for detecting contamination in NLP datasets and Large Language Models (LLMs). It offers a comprehensive suite of methods, ranging from string matching to model likelihood and embedding similarity, to ensure data integrity. This tool is crucial for researchers and developers working with LLMs to maintain the reliability of their models and evaluations.
files-to-prompt: Concatenate Files into a Single Prompt for LLMs
files-to-prompt is a command-line tool designed to concatenate the contents of multiple files from a directory into a single, structured prompt suitable for Large Language Models (LLMs). It offers flexible options for filtering files by extension, ignoring patterns, and supports various output formats including standard text, Claude XML, and Markdown. This utility streamlines the process of preparing complex codebases or documentation for AI analysis.
android-mcp-server: Programmatic Android Device Control via ADB
The android-mcp-server is a Python-based Model Context Protocol (MCP) server designed to offer programmatic control over Android devices using ADB. It enables advanced device management capabilities, including screenshot capture, UI layout analysis, and package management. This server integrates seamlessly with MCP clients like Claude Desktop, providing a powerful tool for developers and automation enthusiasts.
Dagster: An Orchestration Platform for Data Assets
Dagster is a powerful open-source orchestration platform designed for the development, production, and observation of data assets. It provides a unified programming model for building and managing data pipelines, making it easier to define, test, and deploy complex data workflows. This platform supports various data engineering, analytics, and machine learning operations.
FBI_Watchdog: Real-time OSINT for Domain Seizures and DNS Monitoring
FBI_Watchdog is a powerful OSINT tool designed for real-time monitoring of domain DNS changes and law enforcement seizures. It provides instant alerts via Telegram and Discord, capturing screenshots of affected sites. This Python-based project helps users stay informed about critical domain modifications and takedowns.
QuantStats: Powerful Python Library for Portfolio Analytics
QuantStats is a comprehensive Python library designed for portfolio profiling, offering in-depth analytics and risk metrics for quants and portfolio managers. It helps users better understand their investment performance through robust statistical analysis and powerful visualization tools. This library simplifies the process of generating detailed performance reports and conducting advanced quantitative analysis.
AIMET: Advanced Quantization and Compression for Neural Networks
AIMET, the AI Model Efficiency Toolkit, is an open-source Python library developed by Qualcomm Innovation Center, Inc. It provides advanced techniques for quantizing and compressing trained deep learning models. This toolkit helps improve runtime performance and reduce memory footprint, making models more efficient for deployment on edge devices while minimizing accuracy loss.
LLM Reasoners: Advanced Library for Large Language Model Reasoning
LLM Reasoners is a powerful Python library designed to significantly enhance the complex reasoning capabilities of Large Language Models. It offers a comprehensive suite of cutting-edge search algorithms, intuitive visualization tools, and optimized performance for efficient LLM inference. The library prioritizes rigorous implementation and reproducibility, making it a reliable tool for researchers and developers in the AI field.
Foxel: Private Cloud Storage with AI-Powered Semantic Search
Foxel is a highly extensible private cloud storage solution designed for both individuals and teams. It offers centralized file management across various storage backends and features powerful AI-powered semantic search. This allows users to easily find content within images, videos, and documents using natural language queries.