10:40 am April 22, 2024 By Julian Horsey Last week Meta (formally Facebook) released its latest large language model (LLM) AI model in the form of Llama 3.

Fine-tuning is critical to improving large language model (LLM) outputs and customizing them to specific enterprise needs. When done correctly, the process can result in more accurate and useful ...

AI models perform only as well as the data used to train or fine-tune them. Labeled data has been a foundational element of machine learning (ML) and generative AI for much of their history.

How to fine tune AI models with no-code 11:45 am July 26, 2024 By Julian Horsey Fine-tuning an AI model is like teaching a student who already knows a lot to become an expert in a specific subject.

Gigabyte has officially released its new local AI model training and fine-tuning utility to work with a new line of AI-targeted hardware. Using the new utility with Gigabyte’s recommended ...

Fine-tuning refers to a process of training a pre-trained AI model on a curated dataset to create a smaller new model, which is then capable of producing more specific kinds of outputs.

The LLM won’t be as knowledgeable as a fine-tuned peer, but RAG can get an LLM up to speed at a much lower cost than fine-tuning. Still, several factors limit what LLMs can learn via RAG.

In my point of view, there are two methods to enhance the quality of coming from Generative AI: prompt engineering and fine-tuning. Understanding how they can help us to unlock the full potential ...

Small businesses can improve their online presence with LLM prompting or AI agents, while global corporations may benefit from RAG for research and LLM fine-tuning for brand consistency.

Fine-tuning’s surprising hidden cost arises from acquiring the dataset and making it compatible with your LLM and your needs. In comparison, once the dataset is ready, the fine-tuning process ...