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...

In the most recent versions Elasticsearch provides semantic searches. This post summarizes how this new functionality can be utilized. Semantic searches allow finding relevant information even if ...

Vector-based searches allow finding semantic relevant information without the presence of keywords. Often vector-based search engines can only handle documents with limited lengths. This post descr...

One of the key challenges of LLMs is hallucination. Retrieval Augmented Generation (RAG) reduces hallucination but cannot eliminate it. This post summarizes a new concept to address this shortcomin...

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...