1-BIT QUANTIZATION

Summary from the TLDR AI newsletter

Extreme low-bit quantization for small pre-trained models, like Llama2-7B, is challenging, but fine-tuning just 0.65% of parameters significantly improves performance. Newly fine-tuned 1-bit models outperform 2-bit Quip# models, while 2-bit models with specialized data can exceed full-precision counterparts. This research suggests that proper fine-tuning and quantization may enhance efficiency without compromising model quality, potentially shifting focus from training smaller models to optimizing larger, quantized ones.