---
title: "Quantization Aware Training (QAT)"
back-to-top-navigation: true
toc: true
toc-expand: 2
toc-depth: 4
---

## Overview

[Quantization Aware Training](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#quantization-aware-training) (QAT) is a technique for improving the accuracy of models which are quantized
by applying "fake" quantizations to the model's weights (and optionally, activations) during training. This fake
quantization allows for the model to adjust for noise introduced by the quantization, so when the model is eventually
quantized, the accuracy loss is minimized. We use the quantization techniques implemented in [torchao](https://github.com/pytorch/ao) to provide
support for QAT and post-training quantization (PTQ) in axolotl.

We recommend reviewing the excellent QAT tutorial in the [torchtune library](https://pytorch.org/torchtune/main/tutorials/qat_finetune.html#quantizing-the-qat-model),
and the QAT documentation in the [torchao library](https://github.com/pytorch/ao/tree/main/torchao/quantization/qat), for more details.

## Configuring QAT in Axolotl

To enable QAT in axolotl, add the following to your configuration file:

```yaml
qat:
  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4", "int8", "float8"
  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are "int4", "fp8", and "nvfp4".
  group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
  fake_quant_after_n_steps: # Optional[int] = None. The number of steps to apply fake quantization after
```

We support the following quantization schemas:

- `Int4WeightOnly` (requires the `fbgemm-gpu` extra when installing Axolotl)
- `Int8DynamicActivationInt4Weight`
- `Float8DynamicActivationFloat8Weight`
- `Float8DynamicActivationInt4Weight`
- `NVFP4`

Once you have finished training, you must quantize your model by using the same quantization configuration which you used to train the model with. You can use the [`quantize`](./quantize.qmd) command to do this.
