Geeky Gadgets

How Unsloth Makes Fine-Tuning LLMs a Breeze to Boost AI Performance

Fine-tuning large language models (LLMs) might sound like a task reserved for tech wizards with endless resources, but the reality is far more approachable—and surprisingly exciting. If you’ve ever ...
Geeky Gadgets

How to fine tune large language models (LLMs) with memories

If you would like to learn more about how to fine tune AI language models (LLMs) to improve their ability to memorize and recall information from a specific dataset. You might be interested to know ...
SiliconANGLE

Thinking Machines launches Tinker language model fine-tuning service

Thinking Machines Lab Inc., the artificial intelligence startup led by former OpenAI executive Mira Murati, today introduced its first commercial offering. Tinker is a cloud-based service that ...
Hosted on MSN

Over-training large language models may make them harder to fine-tune

A small team of AI researchers from Carnegie Mellon University, Stanford University, Harvard University and Princeton University, all in the U.S., has found that if large language models are ...
Nature

Fine-tuning protein language models boosts predictions across diverse tasks

Prediction methods inputting embeddings from protein language models have reached or even surpassed state-of-the-art performance on many protein prediction tasks. In natural language processing ...
Morning Overview on MSN

Large AI models learn by tuning billions of internal settings called parameters

Researchers at OpenAI trained a single language model on 175 billion learned numerical weights, each one adjusted during training to predict the next word in a sequence. That model, GPT-3, ...
Nature

Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT

Single-cell large language models (scLLMs) capture essential biological insights from vast single-cell atlases but struggle in out-of-context applications, where zero-shot predictions can be ...
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

Researchers warn of 'catastrophic overtraining' in LLMs

A new academic study challenges a core assumption in developing large language models (LLMs), warning that more pre-training data may not always lead to better models. Researchers from some of the ...
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 ...
ZDNet

OpenAI's budget GPT-4o mini model is now cheaper to fine-tune, too

A popular strategy for engaging with generative AI chatbots is to start with a well-crafted prompt. In fact, prompt engineering is an emerging skill for those pursuing career advancement in this age ...
eWeek

9 Best Large Language Models For Your Tech Stack

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...