PEFT

• Key points are in the visual diagram above.

Standardizes fine-tuning workflows. One API for LoRA, QLoRA, and adapter management across models.

PEFT is a universal adapter kit — fits different appliance brands with the same installation process.

Scenario

Swap customer-support LoRA adapter for sales LoRA adapter on same base model — no redeploy of full weights.

Solution

In Model Engineering (Awareness), apply PEFT to this scenario: Swap customer-support LoRA adapter for sales LoRA adapter on same base model — no redeploy of full weights. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to PEFT (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where PEFT happens in the code.

1# PEFT — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for PEFT8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain peft clearly."},11        {"role": "user", "content": f"What is peft?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating PEFT as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for peft

Quick recap — the most important points from this module.