Fine-tuning, LoRA, & QLoRA
- saurabhkamal14
- 3 days ago
- 2 min read
1. Full Fine-Tuning (The "Total Brain Rewrite")
In this method, you update the entire AI "brain" (the Base Model) at once.
How it works: Every single connection in the model is modified to learn the new task.
The cost: Because it uses 32-bit and 16-bit processing for every single part of the model, it requires massive amount of computer memory and power.
Analogy: It's like rewriting an entire 500-page textbook just to add one new chapter. It's like effective, but it takes a huge amount of effort and paper.
2. LoRA (The "Sticky Notes" Method)
LoRA stands for Low-Rank Adaption. Instead of changing the whole base model, you leave the original "brain" alone and add small, extra layers called Adapters.
How it works: The Base Model stays "frozen" (unchanged). You only train the small 16-bit adapters to learn the new specific skill.
The benefit: It is much faster and uses far less memory because you are only updating a tiny fraction of the system.
Analogy: Instead of rewriting the textbook, you leave it as-is and just add a few sticky notes on specific pages with the new information.
3. QLoRA (The "High-Efficiency" Method)
QLoRA is a more advanced version of LoRA that makes the process even smaller and cheaper to run.
4-bit compression: It "quantizes" (compresses) the Base Model down to 4-bit, making it take up significantly less space than the original.
Paging Flow: It uses a clever trick to move data to the CPU if the main memory is full, which prevents the computer from crashing.
The Benefit: This allows you to fine-tune very powerful AI models on a single, regular computer instead of needing a giant server room.
Analogy: You compress the entire text-book into a tiny pocket-sized version, but you still keep your detailed sticky notes for the new information.
Method | Memory Usage | Speed | Cost |
Full Finetuning | Very High | Slow | Expensive |
LoRA | Low | Fast | Cheap |
QLoRA | Very Low | Fast | Very Cheap |
