Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.
Large Language Models have a reputation for being expensive to train.
When GPT-style models first became popular, fine-tuning meant updating every weight in the network. If your model had 7 billion parameters, you trained 7 billion parameters. If it had 70 billion parameters, you trained 70 billion parameters.
For most teams, that was simply impractical.
Then researchers realized something surprising: you often don't need to modify the entire model to teach it new skills. In many cases, you can freeze almost all of the model and train only a tiny fraction of additional parameters.
