My motivation for creating tools often stems from a desire to get familiar with new technologies. This project was no different; I wanted to deepen my understanding of Generative AI. However, this wasn’t the primary reason for its creation. The real driving force was a persistent gap in my workflow that I couldn’t ignore any … Read More
In my previous post, Experiences in Fine-Tuning LLMs: Time + Power = Potato?, I covered my experiences around trying to fine-tune an LLM (large language model) with a dataset, which gave me less than stellar results. Ultimately, fine-tuning is best for a use-case where additional reasoning & logic needs to be added to an LLM, … Read More
Embarking on the journey to fine-tune large language models (LLMs) can often feel like setting sail into uncharted waters, armed with hope and a map of best practices. Yet, despite meticulous planning and execution, the quest for improved performance doesn’t always lead to the treasure trove of success one might anticipate. And I know you … Read More
If you’ve tried to use Ollama with Docker on an Apple GPU lately, you might find out that their GPU is not supported. But you can get Ollama to run with GPU support on a Mac. This article will explain the problem, how to detect it, and how to get your Ollama workflow running with all of your VRAM (which, on a Mac, is your DRAM too)!
So you want to execute some custom CUDA-based AI processing on a GPU, but don’t have the hardware? Have an AWS account? Try using the DLAMI machine instances. This article explains how to get started if you need OS-level access.
This article logs my experiments with the AI Assistant, a Generative AI service from JetBrains that keeps you in the IDE, asking questions of an expert chatbot. The service provides a pane that is docked alongside of your coding tools, so you don’t have to keep jumping out to Google to grab a code snippet. It also provides some refactoring features as well. Read on for more information.
