Making large AI models cheaper, faster and more accessible
-
Updated
Jul 4, 2024 - Python
Making large AI models cheaper, faster and more accessible
LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training
Modular text transformation system, for AI based and regular expression workflows.
Internet scanning anywhere and everywhere. Globally deploy your Internet measurements, scans and experiments leveraging cloud infrastructure and consumer-grade VPN subscriptions.
MatrixWorld: A pursuit-evasion platform for safe multi-agent coordination and autocurricula
A face database for large scale face recognition
A high-performance distributed training framework for Reinforcement Learning
飞桨大模型开发套件,提供大语言模型、跨模态大模型、生物计算大模型等领域的全流程开发工具链。
Large-scale cell segmentation with cellpose.
DSIR large-scale data selection framework for language model training
[MICCAI'23] Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train
Split large docs for efficient LLM processing. Inspired by MapReduce, it tackles scalability & empowers LLMs to analyze massive text datasets.
A Really Scalable RL Framework to 10k+ CPUs
[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition
LTB-Symm is a publicly available code that does two things: large scale tight-binding (LTB) calculation of 2D materials, and checks topological symmetries (Symm) of their wave functions.
Learn how to design large-scale systems. Prep for the system design interview.
Automated pipeline for large scale detection of solar arrays in France
Measure the time for large-scale operations and contribute to the exploration of computational efficiency.
Python implementation of RLS-Nystrom
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
Add a description, image, and links to the large-scale topic page so that developers can more easily learn about it.
To associate your repository with the large-scale topic, visit your repo's landing page and select "manage topics."