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Fine-Tuning & Model Customization

Course Contents

Prompting vs RAG vs Fine-Tuning — The Decision

Good Reasons to Fine-Tune

Bad Reasons to Fine-Tune

Fine-Tuning + RAG Together

Estimating Cost & Effort

How Training Actually Works (Backprop Recap)

The Memory Bottleneck & Memory Math

Numerical Precision & Quantization (GGUF/llama.cpp, GPTQ, AWQ)

Full Fine-Tuning vs PEFT

LoRA & QLoRA Explained

Fine-Tuning Hyperparameters & Tactics

Data Quality, Coverage & Quantity

Sourcing & Annotating Data

Synthetic Data Generation

Model Distillation

Cleaning, Dedup & Formatting

Hands-On: Build a Fine-Tuning Dataset

The Post-Training Pipeline: SFT → DPO → RL

Direct Preference Optimization (DPO)

Reasoning Fine-Tuning with GRPO

Model Merging (SLERP, TIES, DARE)

Evaluating a Fine-Tuned Model

Hands-On: Fine-Tune a Small Model with LoRA

Estimating Cost & Effort

Estimate what a fine-tune really costs — the four budget lines, why compute is the smallest thanks to LoRA and QLoRA, data as most of the effort, serving and maintenance, and managed versus self-hosted.

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