GitHub

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

AWQ search for accurate quantization. Pre-computed AWQ model zoo for LLMs (LLaMA-1&2, OPT, Vicuna, LLaVA; load to generate quantized weights). Memory-efficient 4-bit Linear in PyTorch. Efficient CUDA ...
GitHub

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

AWQ search for accurate quantization. Pre-computed AWQ model zoo for LLMs (LLaMA, OPT, Vicuna, LLaVA; load to generate quantized weights). Memory-efficient 4-bit Linear in PyTorch. Efficient CUDA ...
IEEE

Efficient Hierarchical Quantization for Heterogeneous Devices in Cloud–Edge–Device ...

Abstract: Cloud-based quantization is a key technique for deploying deep neural networks on resource-constrained devices. However, the growing number of heterogeneous devices has placed an increasing ...
IEEE

SearchQ: Search-Based Fine-Grained Quantization for Data-Free Model Compression

Abstract: The huge memory and computing costs of deep neural networks (DNNs) greatly hinder their deployment on resource-constrained devices with high efficiency. Quantization has emerged as an ...
知乎 on MSN

学transformer前需不需要先把RNN学一遍?

直接给结论，不用。 甚至可以说，都要2026年了，如果你现在还抱着十年前的教材，非要先啃明白RNN，再搞懂LSTM里那个该死的遗忘门，最后才敢翻开Transformer的第一页，那你纯粹是在浪费生命。