Xmas morning conversation on LLM Fine-tuning :-)

A few months ago, I published an article titled, "LLM Fine-tuning is like training an intern". Last week after reading the article, a Bio-Medical engineering postgrad student reached out, seeking help with fine-tuning.

(LinkedIn messages from last week)

Bio-med student: Raj I loved your article but can you dive a bit deeper?

Raj: Let's work backward - what is your objective?

Bio-med student: I have a problem that I think I might be able to solve by fine-tuning a model. But I don't know enough about Generative AI to make a decision on whether it is do-able or not. Help me understanding, what do I need to do for fine-tuning a large language model?

Raj : Let's get on a call - I am available on Dec 25th anytime after 10:00 AM ET

Bio-med student: GREAT - Sent you an invite for video call

TL;DR ALERT... watch this video instead

Past article that prompted this discussion : LLMs Are Like New Interns

(A quick recap from my previous article)

Imagine you’ve just hired an intern. They’ve graduated from a reputable university with a solid understanding of general principles in your field. However, to contribute effectively to your company, they need specialized training—your systems, your processes, your way of working (and even thinking).

Fine-tuning an LLM works the same way. LLMs come pre-trained on a vast amount of general knowledge. They’re your “college-educated” intern, but they don’t know your specific business context. Fine-tuning is how you train this intern to excel in tasks unique to your organization.

Here is my cleaned up response (from a video call on X-Mas morning)

I will continue this discussion with the intern analogy I used in my last article. Let's think step by step, on how you would select and train an intern.

Step 1: Selecting the Right Intern (Model)

When hiring an intern, you think about their fit for the job. Similarly, the fine-tuning process begins by selecting a base model. Here’s what you should consider:

• Task Requirements: If the task requires only 80% accuracy, you don’t need a PhD-level intern—opt for a smaller, more efficient model.
• Privacy Needs: If your data is sensitive, a self-hosted, open-source model is like hiring an intern in-house rather than outsourcing tasks to an external agency (unless you trust the external agency)
• Cost Considerations: Think of the trade-offs—third-party APIs for closed-source models can add up, while self-hosted open-source options depend on your infrastructure.
• Community Support: A model backed by a strong community is like an intern with a robust alumni network—help is always around the corner.
• Longevity: Choose a model that isn’t going to “graduate and leave.” Make sure the provider is committed to updates and support.

Step 2: Setting Expectations (Defining Evaluation Criteria)

Before training begins, you set expectations for your intern: “Here’s what success looks like.” e.g., "you should be able to finish XYZ task with 99% accuracy within 3 hours". For fine-tuning, this involves defining an evaluation strategy upfront, much like test-driven development approach in software.

• Create a test dataset that includes edge cases, ensuring the model can handle real-world challenges.
• Automate evaluation where possible, but recognize when human feedback is needed—especially for nuanced tasks like summarizing legal documents.
• For scalability, consider using a larger LLM to evaluate the fine-tuned model’s outputs (LLM-as-a-Judge).

Step 3: Prepping the Training Material (Dataset Preparation)

Your intern needs quality resources to learn from. Similarly, the dataset you prepare for fine-tuning makes or breaks the process.

• Clean the Data: Remove duplicates and irrelevant information.
• Format Appropriately: Align the data with the model’s requirements.
• Balance the Dataset: For tasks like fraud detection, include an equal number of positive and negative examples to prevent bias.
• Augment When Needed: If you’re short on data, use augmentation techniques to generate synthetic samples.

Step 4: On-the-Job Training (Fine-Tuning)

This is where the real learning happens. Using a fine-tuning library or the model provider’s API, you “train” the intern (or model) to handle your specific tasks. Like adjusting the pace of training for your intern, you may need to tweak hyperparameters during the process.

Step 5: Reviewing Performance (Evaluation)

After training, you give your intern a trial run:

• Use your pre-defined evaluation criteria to measure success.
• If the intern isn’t performing as expected, revisit their training materials or adjust their instructions.

For LLMs, this iterative process might involve improving the dataset, adjusting hyperparameters, or—if all else fails—choosing a different base model.

Illustration : Depicting the process

The Fine-Tuning Payoff

Fine-tuning, like mentoring an intern, requires effort and iteration. But once done, your model—or intern—is not just functional; it’s tailored to your unique needs.

So, whether you’re managing a team or building the next AI-powered application, think of fine-tuning as an investment in specialized capability. And remember: even the best intern started somewhere!

Raj : Do you think, fine-tuning will help solve your problem?

Biomed student: I think so, but I will need to digest all of the information you have provided. Let me do a bit of self-study and get back to you

Raj : Absolutely, let me know whenever you are ready !! BTW I still don't know your domain specific problem, so next time be prepared to educate me :-) Merry Xmas and a very happy new year to you !!

Biomed student: Thanks and same to you

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