I want to share my experience in the cloud, AI, and agents development areas.
The blog post demonstrates using the ChatWatsonx class of langchain_ibm for "function calls" with LangChain and IBM watsonx™ AI. It provides an example of a chat function call for weather information for various cities. The post also includes instructions to set up and run the example. Additional resources and examples are also provided.
The blog post introduces InstructLab, a project by IBM and Red Hat, outlining the fine-tuning process of the model "MODELS/MERLINITE-7B-LAB-Q4_K_M.GGUF." This involves data preparation, model training, testing, and conversion, finally serving the model to verify its accuracy, by using a personal musician example.
This blog post provides a step-by-step guide to setting up InstructLab CLI on an Apple Laptop with an Apple M3 chip, including an overview of InstructLab and its benefits. It also mentions supported models and detailed setup instructions. Additionally, it refers to a Red Hat YouTube demonstration and highlights the project's potential impact.
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.
Converting PDF to JSON is a simple task that uses Python. Converting can be helpful in various pre-processing situations involving data.
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.
This blog post explores managing local filesystem access with Podman Desktop on macOS. It discusses two options: mapping an existing local folder as a volume to the container, and creating a local Podman volume and accessing data inside the container. Detailed steps for both approaches are provided, addressing potential access rights issues.
This blog post discusses how to migrate from org.json to Gson. It provides code examples for both libraries and concludes that the migration requires minimal changes.
This blog post demonstrates the Java Spring Boot implementation to invoke a watsonx.ai endpoint. It outlines the classes and steps involved, including building and sending requests, handling prompts, and extracting answers. The post also provides sample code for invoking the endpoint and using RestTemplate. Overall, it offers a comprehensive guide on utilizing watsonx.ai in a Spring Boot application.
Thomas Suedbroecker's Blog 