This post explains what ‘instruct’ versions of Large Language Models are and how instructions can be used for efficient fine-tuning. Often there are instruct versions of popular Large Language Mod...

Classic fine-tuning of Large Language Models typically changes most weights of the models which requires a lot of resources. LoRA based fine-tuning freezes the original weights and only trains a sm...

There are several ways to run Python code locally. Often it is desired to run it in virtual environments and containers to be able to run multiple configurations in parallel and to easily remove co...

Watsonx.ai is IBM’s offering to train, validate, tune, and deploy generative AI based on foundation models. It comes with several open source models which are briefly described in this post. As a ...

The Hugging Face libraries have become the de-facto standard how to access foundation models from Python, both for inference and fine-tuning. This post describes how to use the Hugging Face APIs fo...

With watsonx.ai, you can train, validate, tune, and deploy generative AI based on foundation models. This post explains some of the strategies to instruct Large Language Models how to generate text...

FLAN-T5 is a Large Language Model which was open sourced by Google at the end of 2022. It has been fine-tuned on multiple tasks, but can be further fine-tuned. This post explains how to do this via...

At IBM Think 2023 several exciting new Foundation Model capabilities have been announced. Below are some of my highlights. Most of the content below is from the great talk from Dr. Darío Gil, IBM ...

To find the best possible models and parameters for Question Answering via Generative AI, a lot of experiments need to be run. While some techniques have been proven successful, other approaches ne...

Transformer based AI models can generate amazing answers to users’ questions. While the underlaying Large Language Models are not retrained, the performance of Question Answering AI can be improved...