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...

Large Language Models can improve search results significantly, since they don’t try to find exact word matches but passages of text that fit best to the questions. This post explains high level in...

Large Language Models can improve the user experience of virtual assistants like Watson Assistant by providing answers rather than lists of links. With Watson Assistant’s ‘Bring your own Search’ fe...