In that paper they frequently say "integer quantization" for a reason. They relax to quantization because it's natural 4-bit NormalFloat Quantization The Nor

In that paper they frequently say "integer quantization" for a reason. They relax to quantization because it's natural 4-bit NormalFloat Quantization The NormalFloat (NF) data type builds on Quantile Quantization[15] which is an information-theoretically optimal data type that ensures each quantization bin has an equal number of values assigned from the input tensor. - QLoRA: Efficient Finetuning of Quantized LLMs https://arxiv.org/abs/2305.14314 3. Float8 Quantized Fine-tuning, for speeding up fine-tuning by dynamically quantizing high precision weights and activations to float8, similar to pre-training in float8. - https://docs.pytorch.org/ao/stable/eager_tutorials/finetuning.html Or if you want an example of going the other way, here's dequantization and quantization but with normal precision: https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial11/NF_image_modeling.html https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_n... Or it's the same quanta in quantum mechanics (which isn't integer!) If you think I'm being pedantic, well... yes... the thread is about math, a formal and pedantic language