Axolotl

Prerequisite: Familiarity with the concepts introduced in Executive Summary. Review it first if the terminology in this part is unfamiliar. ← Series hub ← Previous | Next → Answer-first: Supervised Fine-Tuning (SFT) data quality determines downstream model capabilities; applying NEFTune noise injection during training improves conversational quality by up to 20%, while SemDeDup vector clustering prunes 30%–50% of redundant data to cut GPU training hours nearly in half without losing model accuracy. ...

Prerequisite: Familiarity with the concepts introduced in Part 2 — Sft Data Engineering. Review it first if the terminology in this part is unfamiliar. Answer-first: QLoRA fine-tuning combines 4-bit NormalFloat (NF4) base model quantization with low-rank adapter matrices ($r=16, \alpha=32$), reducing VRAM usage from 80GB to under 10GB and accelerating training speed by 4.5x using Unsloth Triton kernels on a single GPU. Full fine-tuning of an 8B parameter model in FP16 precision requires updating 8 Billion weights simultaneously. This demands over 80GB of GPU VRAM for model weights, activation memory, and AdamW optimizer states, forcing teams to lease expensive multi-GPU cluster nodes (8x A100/H100). ...