๐๐ถ๐ด๐ต-๐๐ฐ๐ฐ๐๐ฟ๐ฎ๐ฐ๐ ๐๐ผ๐-๐ฃ๐ฟ๐ฒ๐ฐ๐ถ๐๐ถ๐ผ๐ป ๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด
Training large AI models costs a lot of money. It requires massive amounts of memory and compute power. You face a choice between speed and accuracy.
Low precision training uses fewer bits to represent numbers. This makes training faster. It uses less memory. It helps you train larger models on smaller hardware.
The problem is that low precision often reduces accuracy. Small errors add up during training. These errors make the model perform poorly.
New methods solve this problem. You get the speed of low precision with the accuracy of high precision.
How it works:
- Use low precision for most calculations.
- Keep a high precision copy of weights.
- Update the high precision weights using low precision gradients.
- This prevents error accumulation.
This approach lets you train models more efficiently. You save time and reduce costs.
Source: https://dev.to/paperium/high-accuracy-low-precision-training-1ppp
Optional learning community: https://t.me/GyaanSetuAi