The Untold Truths of Local LLM Training: It’s Not All Sunshine and Pip Installs
T raining and fine tuning your own local Large Language Model (LLM) sounds cool, right? YouTube tutorials make it seem like a breeze: pip install a few things, feed in some data, and voila! You’ve got your very own AI companion. But hold on to your GPUs, because there’s a lot they don’t tell you.
You fire up your terminal, type “pip install” followed by some magical-sounding libraries, and… nothing! Module not found? Welcome to the wonderful world of dependency conflicts. Those YouTube tutorials often gloss over the intricate dance of libraries needed for LLM training. Be prepared to spend hours untangling version mismatches and compatibility issues.
Most local training is a myth. LLMs require immense computational power. While your fancy M1 Mac might purr along valiantly, it’s just not enough. The reality? You’ll likely be training on Google Colab, Vast.ai, or some other cloud virtual machine, which means — surprise! — you need a network connection, and guess what? You’re sending your data out there.
The real challenge lies in the messy prep work. Forget waiting for the “fine-tuning” stage, which is comparatively smooth sailing. You’ll spend most of your time wrestling with dependencies, pre-processing your data, and ensuring everything plays nicely together.
