Low-bit quantization techniques can be adapted for diverse and complex LLM architectures beyond Transformer-based models by employing several strategies. First, it is essential to develop quantization methods that are architecture-agnostic, allowing them to be applied to various neural network designs, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and hybrid models that integrate multiple architectures. This can be achieved by focusing on the fundamental principles of quantization, such as weight and activation compression, while ensuring that the quantization algorithms can accommodate the unique characteristics of different architectures.
Second, custom low-bit number formats and quantization granularities can be designed to suit the specific needs of non-Transformer models. For instance, RNNs may benefit from dynamic quantization techniques that adapt to the temporal dependencies in sequential data, while CNNs might leverage spatial quantization strategies that consider the locality of features. By tailoring the quantization approach to the architecture's operational patterns, it is possible to maintain model performance while achieving significant reductions in memory and computational requirements.
Additionally, integrating low-bit quantization with advanced training techniques, such as quantization-aware training (QAT) and post-training quantization (PTQ), can enhance the robustness of quantized models across various architectures. These methods can be fine-tuned to account for the specific distribution of weights and activations in different model types, ensuring that the quantization process minimizes accuracy loss.
Finally, leveraging multi-modal architectures that combine language, vision, and other modalities can also inform the development of low-bit quantization techniques. By analyzing how different modalities interact and influence each other, researchers can create quantization strategies that optimize performance across multiple data types, leading to more efficient and effective LLMs.