Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit

LCLMs compress LLM context before decode — 8.8x faster at 16x compression, beating every KV cache method tested. Open-sourced by NYU and Columbia.
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 ...
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 ...
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

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 ...
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 ...