QLoRA

70B models don't fit in GPU memory. QLoRA makes fine-tuning accessible without a GPU cluster.

QLoRA is compressing a textbook to pocket size but still being able to add your own notes in the margins.

Scenario

QLoRA fine-tune Mistral 7B on legal docs using 1x RTX 4090 — 24GB VRAM sufficient.

Solution

In Advanced AI, apply QLoRA to this scenario: QLoRA fine-tune Mistral 7B on legal docs using 1x RTX 4090 — 24GB VRAM sufficient. 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 QLoRA (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 QLoRA happens in the code.

1# QLoRA — 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 QLoRA8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain qlora clearly."},11        {"role": "user", "content": f"What is qlora?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

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

Quick recap — the most important points from this module.