A practical, end-to-end tutorial on instruction tuning LLMs: data design, LoRA/QLoRA training code, evaluation, deployment, and production tips.
A practical, data-driven guide comparing prompting vs. fine-tuning for LLM apps, with decision checklists, trade-offs, and implementation tips.
Step-by-step QLoRA guide with concepts, setup, memory tips, and code to fine-tune LLMs using 4-bit quantization on a single GPU.
A step-by-step LoRA fine-tuning guide with theory, setup, classic LoRA and QLoRA code, evaluation, merging, and practical tips.
A step-by-step guide to preparing high-quality datasets for LLM fine-tuning, from sourcing and cleaning to formats, safety, splits, and evaluation.
A practical guide to choosing RAG vs fine-tuning, with a clear decision framework, patterns, code sketches, and pitfalls.
