I want to share my experience in the cloud, AI, and agents development areas.
The popularity of llama.cpp and optimized GGUF format for models is growing. This post outlines steps to run "Phi-3-Small-128K-Instruct" in GGUF format with llama.cpp on an IBM Cloud VSI with GPUs and Ubuntu 22.04. It covers VSI setup, CUDA toolkit, compilation, Python environment, model usage, and additional resources.
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.
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.
Thomas Suedbroecker's Blog 