· LLM Finetuning · 2 min read
Efficient PEFT Techniques: LoRA and QLoRA Explained
Training a 70B parameter model was once impossible for most. Low-Rank Adaptation (LoRA) changes the game, enabling training on consumer GPUs.
Just recently, training a huge AI model required a supercomputer costing tens of thousands of dollars per run. Today, thanks to new techniques, we can do it on a single powerful desktop. This revolution relies on two key acronyms: LoRA and QLoRA.
LoRA: Low-Rank Adaptation
When we train a model, we are usually updating billions of numbers (weights). LoRA is a clever trick that freezes the main model and just adds a few tiny, trainable layers on top.
- Instead of updating 100 million parameters, we might only update 10,000.
- The Result: We get almost exactly the same performance as full training, but we use a tiny fraction of the memory.
QLoRA: Making it even leaner
Researchers took it a step further. Even with LoRA, you still need to load the huge base model into memory. QLoRA solves this by squashing the base model down (quantising) to make it 4x smaller, while still keeping the training precise.
- The Impact: You can now train a massive “State of the Art” model on typical hardware.
Why This Matters for Business
This efficiency means we can create many specialised “Adapter” models for different parts of your business (a Legal Adapter, an HR Adapter, a Code Adapter) that all plug into the same core brain. It allows for modular, affordable AI.
At Alps Agility, we use these techniques to build custom AI models for clients without the custom price tag.
High performance, low cost. Discover how efficient training can make custom AI affordable. Get a quote.
