Recently, I was curious to see how easy it would be to run run Llama2 on my MacBook Pro M2, given the impressive amount of memory it makes available to both CPU and GPU. This led me to the excellent llama.cpp, a project focused on running simplified versions of the Llama models on both CPU and GPU. The process felt quite straightforward except for some instability in the llama.cpp repo just as I decided to try it out, and which has been fixed in the meantime. Incidentally, this prompted me to document the whole process, just in case I want to do it again in the future. ...

An experiment with prompt injecting Bing Chat – successfully changing its persona, exploring data extraction potential, limitations, and future implications.

My top 3 tips for working better, faster, and just a bit stronger with Azure ML Compute Instances

A quickstart guide to deploying machine learning models in production using Azure Machine Learning’s managed online endpoints

A guide to creating GPU compute instances on Azure ML, installing Stable Diffusion, and running AUTOMATIC1111’s Web UI.

Because life’s too short to deploy things manually

A glimpse of the upcoming paradigm shift in how we do development

The issue with machine learning pipelines is that they need to pass state from one step to another. When this works, it’s a beautiful thing to behold. When it doesn’t, well, it’s not pretty, and I think the clip below sums this up pretty well. made a Rube Goldberg machine pic.twitter.com/gWRNnmm5Ic — COLiN BURGESS (@Colinoscopy) April 30, 2020 Azure ML Pipelines are no stranger to this need for passing data between steps, so you have a variety of options at your disposal. This means it’s not always easy to find the best one, and I’ve often seen people confused when trying to pick the best option. So I wrote this article to try and clear some of that confusion. ...

A story about love, loss, and caching

How to create a model based on an Azure AutoML-trained baseline, using standard open-source components where possible and adapting AutoML specific code where needed