AutoRound: Precise low-bit quantization for large language models #EfficientQuantization

The article discusses the difference between quantization-aware training and post-training quantization in reducing the size of large language models (LLM). Quantization methods are used to compress these models, with recent advancements leading to better low-bit quantization techniques. One example is AQLM, which achieves 2-bit quantization while maintaining the model’s accuracy. This development is significant as it allows for more efficient storage and deployment of LLMs without sacrificing performance.

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Source link: https://towardsdatascience.com/autoround-accurate-low-bit-quantization-for-llms-305ddb38527a?source=rss—-7f60cf5620c9—4