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 ...
Forbes

The Surprising Idea That Generative AI Might Be Better Off Using Visual Images Of Text Rather Than Pure Text As Tokens

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. For anyone versed in the technical underpinnings of LLMs, this ...
VentureBeat

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

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 ...
ZDNet

IBM to test Southeast Asian LLM and facilitate localization efforts

IBM has inked an agreement with AI Singapore (AISG) to test the latter's Southeast Asian large language model (LLM) and make it available for developers to build customized artificial intelligence (AI ...
Geeky Gadgets

How to Fine Tune your own LLM using LoRA (on a Custom dataset)

Imagine unlocking the full potential of a massive language model, tailoring it to your unique needs without breaking the bank or requiring a supercomputer. Sounds impossible? It’s not. Thanks to ...
VentureBeat

This new framework lets LLM agents learn from experience, no fine-tuning required

A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...
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

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale. High inference latency and ...
Semiconductor Engineering

Systematic Analysis of CPU-Induced Slowdowns in Multi-GPU LLM Inference (Georgia Tech)

A new technical paper, “Characterizing CPU-Induced Slowdowns in Multi-GPU LLM Inference,” was published by the Georgia ...