machine-learning

Active Learning for Text Classification in Python

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

For automating the creation of large batches of AI-generated artwork locally.

Chain together LLMs for reasoning & orchestrate multiple large models for accomplishing complex tasks

Tools for understanding how transformer predictions are built layer-by-layer

A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.

AI-in-a-Box leverages the expertise of Microsoft across the globe to develop and provide AI and ML solutions to the technical community. Our intent is to present a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction.

TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.

This repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

A curated list of AI tools, libraries, and resources for economics research, teaching, and policy analysis. Maintained by the OpenEcon team.

Port of MiniGPT4 in C++ (4bit, 5bit, 6bit, 8bit, 16bit CPU inference with GGML)

🐦‍🔥 LangGraph for Swift — build stateful AI agent workflows natively on Apple/Linux platforms.

A flexible, adaptive classification system for dynamic text classification

Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and Redis as a vector database/semantic cache.

Vector Hub - Library for easy discovery, and consumption of State-of-the-art models to turn data into vectors. (text2vec, image2vec, video2vec, graph2vec, bert, inception, etc)

Official implementation of Würstchen: Efficient Pretraining of Text-to-Image Models

Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.

reference architecture for building a travel application with GPT3

Python + Inference - Model Deployment library in Python. Simplest model inference server ever.