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 ...
VentureBeat

Uh-oh! Fine-tuning LLMs compromises their safety, study finds

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More As the rapid evolution of large language models (LLM) continues, ...
Geeky Gadgets

Easily Fine-Tune AI Models Like a Pro with Google Tunix

Have you ever wondered how to transform a general-purpose language model into a finely tuned expert tailored to your specific needs? The process might sound daunting, but with the right tools, it ...
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 ...
Geeky Gadgets

Unlock the Full Power of DeepSeek R1 by Fine-Tuning Its Reasoning Tasks

Fine-tuning a large language model (LLM) like DeepSeek R1 for reasoning tasks can significantly enhance its ability to address domain-specific challenges. DeepSeek R1, an open source alternative to ...
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 ...
XDA Developers on MSN

I fine-tuned a 7B model to write my Home Assistant automations, and it actually works

It'll even run on a GPU with 8GB of VRAM!
Semiconductor Engineering

Largest High-Quality Verilog Dataset for LLM Fine-Tuning (Univ. of Florida)

A new technical paper titled “VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation” was published by researchers at the University of Florida.