The context windows of Large Language Models define how much input can be provided in prompts. Fine-tuning of LLMs with longer context windows is resource intensive and expensive. With the new Long...

IBM announced the general availability of the first models in the watsonx Granite model series which are decoder based Foundation Models trained with enterprise-focused datasets curated by IBM. IB...

LangChain allows chaining and orchestrating LLMs tasks for scenarios like Retrieval Augmented Generation and agents. Below is a snippet that shows how Watsonx.ai models can be accessed in LangChain...

Large Language Models are extremely powerful, but they can only return data that existed when they were trained and they cannot invoke APIs and business logic. The technique ReAct combines Chain-of...

Fine-tuning of Large Language Models optimizes models for certain AI tasks and/or improves performance for smaller and less resource intensive models. This post describes how to further improve mod...

LoRA-based fine-tuning of Large Language Models freezes the original weights and only trains a small number of parameters making the training much more efficient. QLoRA goes one step further and re...

Fine-tuning large language models with instructions is a great technique to customize models efficiently. This post explains briefly how data can be turned into instructions. In my earlier post In...

Serving AI models is resource intensive. There are various model inference platforms that help operating these models as efficiently as possible. This post summarizes two platforms for classic ML a...

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...

Many of the leading Large Language Models only support limited languages currently, especially open-source models and models built by researchers. This post describes some options how to get these ...