NextBigFuture

Tokens and Tokenization are an Important for Fundamental LLM Understanding

Tokens are the fundamental units that LLMs process. Instead of working with raw text (characters or whole words), LLMs convert input text into a sequence of numeric IDs called tokens using a ...
eWeek

How to Train an LLM: A Simple, User-Friendly Guide

In this episode of eSpeaks, Jennifer Margles, Director of Product Management at BMC Software, discusses the transition from traditional job scheduling to the era of the autonomous enterprise. eSpeaks’ ...
InfoWorld

The limitations of model fine-tuning and RAG

The hype and awe around generative AI have waned to some extent. “Generalist” large language models (LLMs) like GPT-4, Gemini (formerly Bard), and Llama whip up smart-sounding sentences, but their ...
TechCrunch

Instead of fine-tuning an LLM as a first approach, try prompt architecting instead

Amid the generative AI eruption, innovation directors are bolstering their business’ IT department in pursuit of customized chatbots or LLMs. They want ChatGPT but with domain-specific information ...
VentureBeat

Fine-tuning vs. in-context learning: New research guides better LLM customization for real ...

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...
Geeky Gadgets

How to fine tune AI models with no-code

Fine-tuning an AI model is like teaching a student who already knows a lot to become an expert in a specific subject. Instead of starting from scratch, we take a model that has learned from a vast ...
InfoWorld

Is creating an in-house LLM right for your organization?

Business leaders have been under pressure to find the best way to incorporate generative AI into their strategies to yield the best results for their organization and stakeholders. According to ...