Fine-Tuning

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). ...

Prompt Engineering vs Fine Tuning vs RAG: Complete 2026 Decision Guide Prompt Engineering vs Fine Tuning: Executive Decision Framework Answer-first: In the prompt engineering vs fine tuning evaluation, prompt engineering offers rapid prototyping with zero setup cost, whereas fine tuning Small Language Models (SLMs) via QLoRA bakes domain knowledge into weights, reducing TTFT latency under 250ms and cutting API token spend by 90%. Small Language Models (SLMs, 1B–8B parameters) combined with fine-tuning and local inference (vLLM) rival proprietary frontier LLMs on specialized domain tasks at a fraction of the cost. The playbook below rests on three architectural choices: ...