Repository History
9 repositories tagged with LLMs
MiroFish: A Universal Swarm Intelligence Engine for Predicting Anything
MiroFish is a cutting-edge AI prediction engine that leverages multi-agent technology to simulate future outcomes. It constructs high-fidelity digital worlds where intelligent agents interact, allowing users to test scenarios and deduce future trajectories. This innovative platform enables predictions across various domains, from public opinion to financial markets and even creative narrative endings.
AI Engineering from Scratch: A Comprehensive Hands-On AI Curriculum
The "AI Engineering from Scratch" repository provides a free, MIT-licensed curriculum for mastering AI engineering from foundational math to advanced agent systems. It emphasizes a hands-on approach, guiding learners to build every algorithm from scratch before utilizing frameworks. With 435 lessons across 20 phases, this project equips students with the practical skills needed to professionally build and deploy AI solutions.
AI-Scientist-v2: Automated Scientific Discovery via Agentic Tree Search
AI-Scientist-v2 is an advanced agentic system designed for automated scientific discovery, capable of generating hypotheses, running experiments, analyzing data, and writing scientific manuscripts. This system has successfully produced the first workshop paper written entirely by AI and accepted through peer review, marking a significant step towards fully autonomous research.
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.
Transformer Lab App: An Open Source Platform for Frontier AI/ML Workflows
Transformer Lab App is an open-source machine learning research platform designed for frontier AI/ML workflows. It provides a comprehensive toolkit for large language models, allowing users to train, tune, and chat on their own machines, whether locally, on-prem, or in the cloud. Backed by Mozilla, this cross-platform application simplifies experimentation with a wide range of models.
GenerativeAICourse: A Comprehensive Hands-On Generative AI Engineering Course
This repository offers a comprehensive, hands-on Generative AI course, starting from fundamental AI concepts to building production-grade applications. It focuses on AI engineering, covering topics like LLMs, RAG, AI agents, and prompt engineering with practical tutorials. The course aims to equip learners with the skills needed to build real-world AI solutions.
Lance: Modern Columnar Data Format for ML and LLMs
Lance is a modern columnar data format, implemented in Rust, designed for machine learning and large language model workflows. It offers significant performance improvements over Parquet for random access, includes vector indexing, and supports data versioning. Compatible with popular tools like Pandas, DuckDB, and PyTorch, Lance streamlines data management for ML applications.
Instructor: Structured Outputs for LLMs with Pydantic and Python
Instructor is a powerful Python library that simplifies extracting structured data from Large Language Models (LLMs). It integrates Pydantic for robust validation, type safety, and IDE support, eliminating the need for manual JSON parsing, error handling, and retries. This tool provides a streamlined and reliable way to get structured outputs from any LLM.
Stagehand: The AI Browser Automation Framework for Production
Stagehand is an innovative AI browser automation framework that expertly blends the precision of code with the adaptability of natural language. Designed for production environments, it empowers developers to choose between writing low-level Playwright code for specific tasks and leveraging high-level AI agents for dynamic interactions. This framework also enhances the automation process with features like action previewing, intelligent caching, and seamless integration with advanced computer use models.