Hardware - TechnoScripts

hardware

How code meets the machine: bits and bytes, memory, CPUs, and GPUs, explained for developers with concrete, beginner-friendly examples.

GenAI: QLoRA Fine-Tuning with Unsloth on a Free Colab GPU

QLoRA is the trick that lets you fine-tune a 7B-class open model on a free Colab Graphics Processing Unit (GPU) without renting a single hour of cloud compute. It stands for Quantized Low-Rank Adaptation, and it stacks two ideas: load the big base model in 4-bit precision so it barely uses memory, then train only a thin set of adapter matrices on top. The other tutorial in this series (fine-tuning LLMs guide) covers the theory of LoRA and QLoRA. This ... Read More

Google Colab GPU Setup: Where to Run Deep Learning for Free

Your laptop does not have to suffer through deep learning. A free Google Colab GPU (Graphics Processing Unit) hands you a real Tesla T4 in a browser tab: no CUDA install, no gaming rig, no bill. This post walks through enabling it, proving it is on with one small check, the session rules that catch beginners, and a timed CPU vs GPU race you can run yourself. “The best way to get started is to stop talking and begin doing.” ... Read More

DL: PyTorch GPU Training, Debugging, and Speedups

A model sitting on the GPU and a batch still on the CPU: that one mismatch kills more first runs than any piece of math. PyTorch GPU training is mostly two habits, keeping everything on the same device and reading error messages instead of panicking at them. This post covers device discipline, CUDA out of memory, shape bugs, NaN loss, and mixed precision speedups. “Neural nets fail silently. The code runs, the loss looks plausible, and the bug just quietly ... Read More