In this blog, we present an end-to-end Quantization-Aware Training (QAT) flow for large language models in PyTorch. We demonstrate how QAT in PyTorch can recover up to 96% of the accuracy degradation on hellaswag and 68% of the perplexity degradation on wikitext for Llama3 compared to post-training quantization (PTQ). We present the QAT APIs in torchao and showcase how users can leverage them for fine-tuning in torchtune.

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The post describes an end-to-end Quantization-Aware Training (QAT) process in PyTorch for large language models. It highlights how QAT can significantly improve accuracy and reduce perplexity degradation compared to post-training quantization (PTQ). Users can leverage QAT APIs in torchao for fine-tuning models in torchtune. Experimental results demonstrate substantial improvements in model performance when QAT is applied, particularly for the Llama3 model. The post also discusses future directions such as mixed-precision quantization, hyperparameter tuning, and extending QAT to other layers and more complex data types.

Quantization-Aware Training for Large Language Models with PyTorch