Model Distillation is a very interesting concept to build small models which are almost as efficient as larger models for specific tasks. This post describes the concept in general and how it can b...

The innovation in the AI space is continuing in an incredible speed. This post describes a new technique that has evolved recently which allows smaller fine-tuned models to almost reach the perform...

Since I’m excited by the incredible capabilities which technologies like ChatGPT and Bard provide, I’m trying to understand better how they work. This post summarizes my current understanding about...

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...