​​QLoRA: Efficient Finetuning of Quantized LLMs Thia paper in | Data Science by ODS.ai 🦜

​​QLoRA: Efficient Finetuning of Quantized LLMs

Thia paper introduces QLoRA, a novel finetuning approach that decreases memory usage significantly, while maintaining impressive performance. Imagine this - a 65 billion parameter model finetuned on a single 48GB GPU, while preserving full 16-bit task performance. This method involves backpropagating gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters, a method that opens up new frontiers in machine learning. The icing on the cake is their high-performing model family, Guanaco, which trumps all previously released models on the Vicuna benchmark, achieving a staggering 99.3% of the performance level of ChatGPT with just 24 hours of finetuning on a single GPU.

The study also unveils several innovative techniques to conserve memory without compromising performance. These include 4-bit NormalFloat (NF4), an innovative data type that is theoretically optimal for normally distributed weights, double quantization for average memory footprint reduction, and paged optimizers to handle memory spikes. The QLoRA approach was applied to finetune more than 1000 models, leading to a detailed analysis of instruction following and chatbot performance across various model types and scales. The results affirm that QLoRA finetuning on a small, high-quality dataset yields state-of-the-art results, even with smaller models than previously used. A notable finding is that GPT-4 evaluations offer a cost-effective alternative to human evaluation. All models and code, including CUDA kernels for 4-bit training, have been released by the researchers.

Paper link: https://arxiv.org/abs/2305.14314
Code link: https://github.com/artidoro/qlora
CUDA kernels link: https://github.com/TimDettmers/bitsandbytes

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-qlora
#deeplearning #nlp #llm #quantization