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Set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
Documentation for Fine Tuning Guide, covering setup, features, and practical usage.
Documentation for Grit, covering setup, features, and practical usage.
Framework that allows for the distributed processing of large data sets across clusters.
Explore useful and relevant data sets for enterprise data science.
A lightweight set of tools for loading and sharing data in data science projects.
Documentation for LearnPrompting, covering setup, features, and practical usage.
Documentation for LLM Evaluation Clarifai Guide, covering setup, features, and practical usage.
Documentation for Miku, covering setup, features, and practical usage.
Documentation for MiniMax, covering setup, features, and practical usage.
A set of functions to support the development of machine learning algorithms.
Accelerate AI training, power complex simulations, and render faster with NVIDIA H100 GPUs on Paperspace. Easy setup, cost-effective cloud compute.
Parris, the automated infrastructure setup tool for machine learning algorithms.
The idea-to-video platform that sets your creativity in motion.
Julia package for loading many of the data sets available in R.
A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.
Documentation for SillyTavern, covering setup, features, and practical usage.
The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together.
Documentation for Tune, covering setup, features, and practical usage.
Documentation for Weights & Biases (Prompts), covering setup, features, and practical usage.
Documentation for Z.AI Developer Platform, covering setup, features, and practical usage.