Title: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization

00 · Course Information
  Outcomes & Rubrics
Week 01 — Adapt, Prompt, or Retrieve: The Model Customization Decision
  Lecture 1 — Choosing Your Customization Path: Prompting, RAG, or Fine-Tuning
  Lab 1 — Three-Way Chatbot Comparison
  Discussion 1 — The Stale Chatbot Dilemma
  Quiz 1 — Adapt, Prompt, or Retrieve: The Model Customization Decision
  Week 01 Resources
Week 02 — Open Models, Licenses, and the Fine-Tuning Toolchain Setup
  Lecture 2 — Open Models, Licenses, and the Fine-Tuning Toolchain
  Lab 2 — Toolchain Setup and Open Model License Audit
  Discussion 2 — Choosing a Model for a Commercial Chatbot
  Quiz 2 — Open Models, Licenses, and the Fine-Tuning Toolchain Setup
  Week 02 Resources
Week 03 — Dataset Design and Data Preparation for Fine-Tuning
  Lecture 3 — Why Less Is Often More: Curating Data for Fine-Tuning
  Lab 3 — Cleaning and Curating a Fine-Tuning Dataset from Dolly-15k
  Discussion 3 — 10,000 Examples or 1,000 Curated Ones?
  Quiz 3 — Dataset Design and Data Preparation for Fine-Tuning
  Week 03 Resources
Week 04 — Instruction and Chat Formatting, Tokenization, and Templates
  Lecture 4 — Chat Templates, Tokenization, and Why Formatting Breaks Fine-Tunes
  Lab 4 — Chat Template Detective: Inspect, Compare, and Preprocess
  Discussion 4 — Debugging Erratic Loss: Is It the Template?
  Quiz 4 — Instruction and Chat Formatting, Tokenization, and Templates
  Week 04 Resources
Week 05 — Supervised Fine-Tuning Fundamentals with Hugging Face TRL
  Lecture 5 — Supervised Fine-Tuning Fundamentals with Hugging Face TRL
  Lab 5 — Fine-Tune and Compare Dataset Formats with TRL SFTTrainer
  Discussion 5 — Diagnosing a Stalled Fine-Tune
  Quiz 5 — Supervised Fine-Tuning Fundamentals with Hugging Face TRL
  Week 05 Resources
Week 06 — Parameter-Efficient Fine-Tuning: LoRA and PEFT
  Lecture 6 — Teaching a Big Model New Tricks Without Retraining All of It
  Lab 6 — LoRA Rank Sweep and Merge Benchmark
  Discussion 6 — Does LoRA Actually Protect You From Forgetting?
  Quiz 6 — Parameter-Efficient Fine-Tuning: LoRA and PEFT
  Week 06 Resources
Week 07 — QLoRA and Training on Consumer GPUs with Unsloth
  Lecture 7 — QLoRA: Fine-Tuning Big Models on Small GPUs
  Lab 7 — Fine-Tune Gemma 270M with Unsloth + QLoRA and Benchmark the Speedup
  Discussion 7 — Scaling QLoRA Up to a 70B Model on a Single RTX 4090
  Quiz 7 — QLoRA and Training on Consumer GPUs with Unsloth
  Week 07 Resources
Week 08 — Axolotl, Config-Driven Training, and Experiment Tracking
  Lecture 8 — Config-Driven Fine-Tuning with Axolotl
  Midterm Exam
  Discussion 8 — Picking a Winner Across Five LoRA Runs
  Week 08 Resources
Week 09 — Preference Tuning: DPO in Practice (with a Look at GRPO)
  Lecture 9 — From RLHF to DPO to GRPO: Preference Tuning Without the RL Headache
  Lab 9 — DPO and GRPO Head-to-Head on a Free Colab T4
  Discussion 9 — DPO, GRPO, or Both?
  Quiz 9 — Preference Tuning: DPO in Practice (with a Look at GRPO)
  Week 09 Resources
Week 10 — Evaluating Fine-Tuned Models and Detecting Overfitting
  Lecture 10 — Evaluating Fine-Tuned Models and Detecting Overfitting
  Lab 10 — Benchmarking Base vs. Fine-Tuned Models with lm-evaluation-harness
  Discussion 10 — Ship It or Iterate? Weighing Trade-offs in Fine-Tune Evaluation
  Quiz 10 — Evaluating Fine-Tuned Models and Detecting Overfitting
  Week 10 Resources
Week 11 — Catastrophic Forgetting, Adapter Merging, and Cloud Training
  Lecture 11 — Why Models Forget, and What LoRA Actually Protects
  Lab 11 — Measuring Forgetting and Merging Adapters
  Discussion 11 — Support-to-Legal Domain Adaptation
  Quiz 11 — Catastrophic Forgetting, Adapter Merging, and Cloud Training
  Week 11 Resources
Week 12 — Quantization Foundations: Precision, GGUF, and llama.cpp
  Lecture 12 — Shrinking Models Without Losing Their Minds: GGUF and Quantization with llama.cpp
  Lab 12 — From Checkpoint to GGUF: Convert, Quantize, and Compare
  Discussion 12 — Quantizing a 70B Model for 8GB of Edge RAM
  Quiz 12 — Quantization Foundations: Precision, GGUF, and llama.cpp
  Week 12 Resources
Week 13 — Advanced Quantization: AWQ, GPTQ, FP8, and KV-Cache Quantization
  Lecture 13 — AWQ, GPTQ, FP8, and the New Quantization Toolbox
  Lab 13 — GPTQ vs. AWQ Shootout with FP8 KV-Cache
  Discussion 13 — Speed, Quality, or Throughput: Picking Your Quantization Strategy
  Quiz 13 — Advanced Quantization: AWQ, GPTQ, FP8, and KV-Cache Quantization
  Week 13 Resources
Week 14 — Knowledge Distillation and Quality-Size Tradeoff Analysis
  Lecture 14 — Teaching Smaller Models to Think Like Bigger Ones
  Lab 14 — Distilling a Causal LM with Transformers and TRL's GKDTrainer
  Discussion 14 — Is the Tradeoff Worth It?
  Quiz 14 — Knowledge Distillation and Quality-Size Tradeoff Analysis
  Week 14 Resources
Week 15 — Serving Adapted and Quantized Models — Handoff to AIINFRA 200
  Lecture 15 — From Fine-Tuning to Serving: Multi-Adapter Inference, Quantization Limits, and the Merge Decision
  Lab 15 — Serve, Compare, and Hand Off Your Fine-Tuned Model
  Discussion 15 — Simple Today vs. Production-Ready at Scale
  Quiz 15 — Serving Adapted and Quantized Models — Handoff to AIINFRA 200
  Week 15 Resources
Week 16 — Capstone Project & Course Review
  Final Capstone Project
  Capstone Presentation
  Course Review & Reflection
  What's Next