AI BriefingNanoQuant: Implementing Sub-1-Bit Quantization
Key point
A new implementation of NanoQuant, which can compress transformer models to sub-1-bit, has been released.
Details
NanoQuant is a post-training quantization (PTQ) method that can compress transformer model weights to 1-bit or even lower (sub-1-bit).
This method maximizes the compression ratio by decomposing a matrix into two scaling vectors and two binary matrices. For example, decomposing an f16 matrix into two binary matrices can theoretically achieve a 16x compression ratio.
It is more efficient than the existing DBF method due to lower computational complexity, but a fine-tuning process is essential to maintain the accuracy of the quantized output.
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