LoRA/QLoRA—Explained From a Business Lens

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TODAY’S DAILY DOSE OF DATA SCIENCE

LoRA/QLoRA—Explained from a business lens​

Consider the size difference between BERT-large and GPT-3:

I have fine-tuned BERT-large several times on a single GPU using traditional fine-tuning:

But this is impossible with GPT-3, which has 175B parameters. That's 350GB of memory just to store model weights under float16 precision.

This means that if OpenAI used traditional fine-tuning within its fine-tuning API, it would have to maintain one model copy per user:

• If 10 users fine-tuned GPT-3 → they need 3500 GB to store model weights.
• If 1000 users fine-tuned GPT-3 → they need 350k GB to store model weights.
• If 100k users fine-tuned GPT-3 → they need 35 million GB to store model weights.

And the problems don't end there:

• OpenAI bills solely based on usage. What if someone fine-tunes the model for fun or learning purposes but never uses it?
• Since a request can come anytime, should they always keep the fine-tuned model loaded in memory? Wouldn't that waste resources since several models may never be used?

​LoRA​ (+ ​QLoRA and other variants​) neatly solved this critical business problem.

The core idea revolves around training a few parameters compared to the base model.

For instance, if the original model has a weight matrix W (shape d*d), one can define the corresponding LoRA matrices A (d*r) and B (r*d).

↳ where r<<<<d (typically, r is a single-digit number).

During fine-tuning, freeze the weight matrix W and update the weights of the LoRA matrices.

During inference, the product of the LoRA matrices results in a matrix of the same shape as W. So one can obtain the output as follows:

This way, every user gets their LoRA matrices, and OpenAI can maintain just one global/common model.

Another good thing is that LoRA matrices usually do not require more than 20-25 MB of memory per user. This is immensely smaller than what we get from traditional fine-tuning.

Lastly, this also solves the other two problems we mentioned earlier:

• If someone fine-tunes the model just for fun or learning purposes but never uses it, it's okay; LoRA matrices are still manageable.
• Loading small LoRA matrices from disk isn't tedious either. These small matrices can be off-loaded if not used for a while and reloaded when needed.

​We implemented LoRA for fine-tuning LLMs from scratch here →​

​LoRA has several efficient variants. We covered them here →​

Moreover, if you want to develop expertise in “business ML,” we have discussed several other topics (with implementations) that align with it:

Here are some of them:

• ​A Crash Course on Graph Neural Networks – Part 1​.
• ​Quantization: Optimize ML Models to Run Them on Tiny Hardware​.
• ​Conformal Predictions: Build Confidence in Your Model's Predictions​.
• ​A Practical Guide to Scaling ML Model Training​.
• ​5 Ways to Test ML Models in Production (Implementation Included)​
• ​Federated Learning: Build Privacy-Preserving ML Models​.
• ​Model Compression: A Step Towards Efficient Machine Learning​.

Why care?

All businesses care about impact. That’s it!

• Can you reduce costs?
• Drive revenue?
• Can you scale ML models?
• Predict trends before they happen?

Thus, the ability to make decisions, guide strategy, and build solutions that solve real business problems and have a business impact will separate practitioners from experts.

👉 Over to you: What are some other benefits of LoRA over traditional fine-tuning?

No-Fluff Industry ML resources to

Succeed in DS/ML roles

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?
• Drive revenue?
• Can you scale ML models?
• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn sophisticated graph architectures and how to train them on graph data in this crash course.
• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
• Run large models on small devices using Quantization techniques.
• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
• Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
• Learn how to scale and implement ML model training in this practical guide.
• Learn 5 techniques with implementation to reliably test ML models in production.
• Learn how to build and implement privacy-first ML systems using Federated Learning.
• Learn 6 techniques with implementation to compress ML models.

All these resources will help you cultivate key skills that businesses and companies care about the most.

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