To combine parameter-efficient adaptation and model compression, we propose AlphaTuning consisting of post-training quantization of the pre-trained language model and fine-tuning only some parts of quantized parameters for a target task.
Our proposed nuQmm reduces the latency of not only each GPU but also the entire inference of large LMs because a high compression ratio (by low-bit quantization) mitigates the minimum required number of GPUs.
Whereas diverse variations of diffusion models exist, extending the linear diffusion into a nonlinear diffusion process is investigated by very few works.
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Specifically, PDM utilizes the flow to non-linearly transform a data variable into a latent variable, and PDM applies the diffusion process to the transformed latent distribution with the linear diffusing mechanism.
Then, as an effort to push the compression ratio to the theoretical maximum (by entropy), we propose a sequential fixed-to-fixed encoding scheme.
When the number of quantization bits is relatively low, however, non-convex optimization is unavoidable to improve model accuracy.
While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time. In this paper, we introduce regularization frequency (i. e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method.
As a practical model compression technique, parameter quantization is effective especially for language models associated with a large memory footprint.
Our analysis shows that for a given number of quantization bits, each block of Transformer contributes to translation quality and inference computations in different manners.
Quantization based on the binary codes is gaining attention because each quantized bit can be directly utilized for computations without dequantization using look-up tables.
Success of quantization in practice, hence, relies on an efficient computation engine design, especially for matrix multiplication that is a basic computation engine in most DNNs.
In this paper, we propose a new network pruning technique that generates a low-rank binary index matrix to compress index data significantly.
Using various models, we show that simple weight updates to comply with compression formats along with long NR period is enough to achieve high compression ratio and model accuracy.
Low-rank approximation is an effective model compression technique to not only reduce parameter storage requirements, but to also reduce computations.
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations.
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs).