Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
Researchers have developed a method to make large language models (LLMs) more accessible by reducing the amount of memory they require. This is achieved through two techniques: bitsandbytes, which reduces the precision of model weights, and QLoRA, which quantizes the activations of the model. The result is a 4-bit version of the LLM that requires significantly less memory than its full-precision counterpart. According to the developers, this could enable deployment on devices
Researchers have developed a method to make large language models (LLMs) more accessible by reducing the amount of memory they require. This is achieved through two techniques: bitsandbytes, which reduces the precision of model weights, and QLoRA, which quantizes the activations of the model. The result is a 4-bit version of the LLM that requires significantly less memory than its full-precision counterpart. According to the developers, this could enable deployment on devices with limited resources.
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Why it matters: This matters because it enables the use of large language models in resource-constrained environments, such as edge devices or embedded systems. This could open up new possibilities for applications like voice assistants, chatbots, and other AI-powered services that require low-latency and high-efficiency processing.
Source: https://huggingface.co/blog/4bit-transformers-bitsandbytes
This article was originally published at: https://huggingface.co/blog/4bit-transformers-bitsandbytes