Fine-tuning allows to adapt generalist models into specialists, boosting their effectiveness for our unique needs. But is it always the best approach?

Quantization is a model compression technique that reduces the size and computational requirements of LLMs. This guide explains how quantization works, its advantages and disadvantages, and practical tips for software developers.

A practical guide to finally understanding most popular LLM benchmarks

What is the difference between base and instruction-tuned models?

Parameter is a key concept in LLMs. This article explains the difference between total and activated parameters.