Fine-tuning LLMs to 1.58bit: extreme quantization made easy
Researchers have developed a method for fine-tuning large language models (LLMs) using extreme quantization, reducing the number of bits required to represent model weights from 32-bit floating-point numbers to just 1.58 bits per parameter. This is achieved through a combination of pruning and knowledge distillation techniques. The approach is made easy by a new library called 'Extreme Quantization' which provides a simple interface for applying this technique to various LLMs
Researchers have developed a method for fine-tuning large language models (LLMs) using extreme quantization, reducing the number of bits required to represent model weights from 32-bit floating-point numbers to just 1.58 bits per parameter. This is achieved through a combination of pruning and knowledge distillation techniques. The approach is made easy by a new library called 'Extreme Quantization' which provides a simple interface for applying this technique to various LLMs, including those on the Hugging Face model hub.
---
Why it matters: This matters because it can significantly reduce the memory requirements and computational costs of large language models, making them more practical for deployment on resource-constrained devices. This is particularly important for applications such as edge AI and mobile devices where power efficiency is crucial.
Source: https://huggingface.co/blog/1_58_llm_extreme_quantization
This article was originally published at: https://huggingface.co/blog/1_58_llm_extreme_quantization