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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

The Memory Bottleneck & Memory Math

Do the GPU memory math for training — the four things living in GPU memory, bytes per parameter, why a 70B model needs about 840 GB to train, activations, and how LoRA and QLoRA collapse the bottleneck.

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