Dr. Nick Ball is a generalist data scientist who joined Paperspace in November 2020. He has been using machine learning since 2000, first in academia (astrophysics), then in Silicon Valley since 2013.
This blogpost shows how to augment a foundation Large Language Model with any webpage or PDF of your own, which effectively turns our LLMs context aware.
In this deep dive, we show how to work with, pretrain, and finetune MosaicML models on Paperspace 8xH100 Machines.
In this tutorial, we show how to fine-tune the powerful LLaMA 2 model with Paperspace's Nvidia Ampere GPUs.
In this article, we walk through the steps for running MLPerf 3.0 on Paperspace GPUs in order to show how we achieve peak performances for AI training, comparable to Nvidia's own reported results.
This tutorial discusses fine-tuning the powerful MPT-7B model from MosaicML using Paperspace's powerful cloud GPUs!
Improve your images versus raw Stable Diffusion with no extra work needed from the user: by adding self-attention guidance, images generated from text prompts are more realistic and nicer to look at.
A new class of deep learning called a generalist model is capable of running on images, text, audio, video and more all at the same time. Here we explore the capabilities of 3 of these models: Perceiver IO, Data2vec, and Gato. We show how to run Perceiver IO on Paperspace.
In this article, we discuss the process of conducting end-to-end data science on Gradient with Nvidia Merlin. This includes walkthroughs on 3 examples: Multi-stage recommenders, training and serving a MovieLens model, and scaling for the massive Criteo dataset.
In this blogpost, we discuss the benefits and utilities of using PyTorch Lightning with Gradient Notebooks to optimize and simplify deep learning code, as well as extend the capabilities of Torch beyond the scope of the original package.
