Fine-tuning and aligning language models to follow instructions requires high quality data and a large quantity of data. IBM published a paper that describes how synthetic data can be generated wit...

Larger Large Language Models like ChatGPT can be prompted to behave as agents for specific use cases. They can return output in certain formats, and they can return instructions to invoke code. Thi...

What a great year 2023 has been! When ChatGPT was published at the end of 2022, I knew it would change the world. I wanted to learn and understand this technology. Fortunately, through my network ...

After Large Language Models have been fine-tuned, the quality needs to be evaluated. This post describes a simple s example utilizing a custom evaluation mechanism. For standard LLM tasks there ar...

Smaller and/or quanitzed Large Language Models can be fine-tuned on a single GPU. For example for FLAN T5 XL (3b) a Nvidia V100 GPU with 16GB is sufficient. This post demonstrates a simple example ...

Before the actual fine-tuning of Large Language Models can start, data needs to be prepared and the code needs to be checked whether it works. This post describes how to do this efficiently locally...

Fine-tuning Large Language Models requires GPUs. When tuning small and/or quantized models, single GPUs can be sufficient. This post explains how to leverage a Nvidia V100 GPU in the IBM Cloud. Ov...

Training and fine-tuning models takes time. During this process it’s important to see progress. This post describes how to visualize output in Tensorboard running locally. The Hugging Face Trainer...

Via Retrieval Augmented Generation search results can be passed as context into prompts to Large Language Models to support the models to generate good responses. Passing the right search results i...

Semantic searches allow finding relevant information even if there are no classic keyword matches. Recent research has shown that combinations of semantic and classic keyword searches often outperf...