This blog post focuses on the importance of Prompt Engineering in AI models, particularly Large Language Models (LLMs), for reducing manual effort and automating validation processes. It emphasizes the need for automation to handle increasing test data and variable combinations, and discusses the use of the Watsonx.ai Prompt Lab for manual and initial automation processes. The post also highlights the significance of integrating automation with version control for consistency and reproducibility.
Fine-tune a large language model (llm) for multi-turn conversations and run it on a Text Generation Inference (TGI) server
This blog post delves into the initial fine-tuning process for large language models (LLMs) for multi-turn conversations and their deployment on Text Generation Inference (TGI) servers. It covers topics such as use cases, data formats, training data preparation, server setup, and evaluation frameworks. The goal is to guide readers through the process of fine-tuning and deploying LLMs.
CheatSheet: Configure the Block Storage usage in Virtual Server Instances on IBM Cloud
This post introduces the use of Block Storage in Virtual Server Instances, particularly in relation to GPUs. It covers the process of mounting and configuring block storage, along with creating, formatting, and mounting the disk. It also provides steps for permanently mounting the storage and attaching existing block storage to a new virtual service instance machine.
