4.6 Fine-Tuning and Dataset Engineering¶

Role at Stage 4: Large Language Models¶

Know when model weights should change and how data quality drives the result. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

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flowchart LR
  P["<b>4.6</b><br/>Fine-Tuning and<br/>Dataset Engineering"]
  P --> S1["<b>4.6.1</b><br/>When to Fine Tune"]
  P --> S2["<b>4.6.2</b><br/>Instruction and<br/>Preference Data"]
  P --> S3["<b>4.6.3</b><br/>PEFT LoRA and QLoRA"]
  P --> S4["<b>4.6.4</b><br/>Fine Tuning Evaluation"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
4.6.1 When to Fine Tune	When to Fine Tune is the working skill inside Fine-Tuning and Dataset Engineering that helps you build the stage artifact, An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases, while collecting enough evidence to trust the result.
4.6.2 Instruction and Preference Data	Instruction and Preference Data is the working skill inside Fine-Tuning and Dataset Engineering that helps you build the stage artifact, An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases, while collecting enough evidence to trust the result.
4.6.3 PEFT LoRA and QLoRA	PEFT LoRA and QLoRA is the working skill inside Fine-Tuning and Dataset Engineering that helps you build the stage artifact, An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases, while collecting enough evidence to trust the result.
4.6.4 Fine Tuning Evaluation	Fine Tuning Evaluation is the working skill inside Fine-Tuning and Dataset Engineering that helps you build the stage artifact, An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how Fine-Tuning and Dataset Engineering supports an llm fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases..
Build and inspect this artifact: Prepare an instruction dataset and PEFT plan.
Measure progress with: Track data quality, coverage, held-out quality, memory, cost, and regressions.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Prepare an instruction dataset and PEFT plan.

Measure: Track data quality, coverage, held-out quality, memory, cost, and regressions.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 4: Large Language Models.