Loss Scaling Free !!link!! -

Recently, however, a new paradigm is emerging to fix the headaches of traditional Mixed Precision: training.

This is primarily achieved through two avenues: loss scaling free

High-precision tasks, such as training Large Language Models (LLMs), often suffer from "spiky" loss curves. Scaling-free formats like BF16 are naturally more robust against these instabilities. Recently, however, a new paradigm is emerging to

# Define the model model = tf.keras.models.Sequential([...]) loss scaling free

# Compile the model model.compile(optimizer='adam', loss=loss_fn)

Loss scaling can be applied to various deep learning tasks, including:

# Compile the model model.compile(optimizer='adam', loss=loss_fn)