Repository History
83 repositories tagged with LLM
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.
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.
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.
RAG-Anything: The All-in-One Multimodal RAG Framework
RAG-Anything is a comprehensive, all-in-one Retrieval-Augmented Generation (RAG) framework designed to process and query diverse multimodal content. It seamlessly handles text, images, tables, and equations within a single integrated system, eliminating the need for multiple specialized tools. Built on LightRAG, this framework offers advanced multimodal retrieval capabilities for complex documents.
Rocketnotes: AI-Powered Markdown Editor for Local and Cloud Knowledge Management
Rocketnotes is an innovative AI-powered markdown editor designed for efficient knowledge management. It allows users to leverage Large Language Models (LLMs) with their documents, offering both 100% local and cloud deployment options. This versatile tool integrates advanced features like semantic search, AI chat, and agentic archiving to enhance productivity and organization.
turboseek: An Open-Source AI Search Engine Inspired by Perplexity
turboseek is an innovative open-source AI search engine developed by Nutlope, drawing inspiration from platforms like Perplexity. Built with TypeScript, it leverages advanced LLMs and search APIs to provide comprehensive answers and related follow-up questions. This project offers a robust foundation for anyone interested in building their own AI-powered search solution.
Obsidian Smart Composer: AI Chat Assistant for Contextual Notes
Obsidian Smart Composer is an AI chat assistant plugin for Obsidian designed to enhance note-taking and content creation. It offers contextual awareness, smart writing assistance, and one-click edits, allowing users to leverage their vault content for more efficient AI interactions. The plugin supports vault-aware conversations, semantic search, and integration with various AI models, including local ones.
vibe-tools: Empowering AI Agents with Teams and Advanced Skills
vibe-tools is a powerful CLI designed to enhance AI agents by providing them with an AI team and advanced skills. It integrates tools like Perplexity for web research, Gemini for repository context, and Stagehand for browser automation. Optimized for Cursor Composer Agent, vibe-tools can be utilized by any coding agent capable of executing commands.
bolt.diy: AI-Powered Full-Stack Web Development with Any LLM in Your Browser
bolt.diy is an open-source project that empowers developers to prompt, run, edit, and deploy full-stack web applications directly in their browser. It offers unparalleled flexibility by supporting over 19 different Large Language Models (LLMs), allowing users to choose their preferred AI for code generation and development tasks. This tool streamlines the development workflow, making AI-assisted coding accessible and highly customizable.
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Paper2Code is an innovative multi-agent LLM system designed to automate the generation of code repositories directly from scientific papers in machine learning. It employs a sophisticated three-stage pipeline, encompassing planning, analysis, and code generation, each managed by specialized agents. This approach ensures faithful and high-quality implementations, outperforming existing baselines on relevant benchmarks.
context-engineering-intro: Master AI Coding Assistants with Context Engineering
Context Engineering represents a powerful evolution beyond traditional prompt engineering, focusing on providing comprehensive information to AI coding assistants for end-to-end task completion. The coleam00/context-engineering-intro repository offers a robust template and step-by-step guide to implement this discipline effectively. It enables developers to leverage AI, particularly with tools like Claude Code, to build complex features with greater consistency and fewer failures.
OmniParser: A Vision-Based Tool for GUI Agent Screen Parsing
OmniParser is a comprehensive tool developed by Microsoft for parsing user interface screenshots into structured, understandable elements. It significantly enhances the ability of vision-based models, such as GPT-4V, to generate accurate actions grounded in specific regions of a GUI. This project aims to advance pure vision-based GUI agents by providing robust screen parsing capabilities.