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IEEE

Quantization via Distillation and Contrastive Learning

Abstract: Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This ...
IEEE

RefQSR: Reference-Based Quantization for Image Super-Resolution Networks

Abstract: Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the ...
GitHub

SDNQ Quantization

SD.Next Quantization provides full cross-platform quantization to reduce memory usage and increase performance for any device. Triton enables the use of optimized kernels for much better performance.
GitHub

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

Thanks to AWQ, TinyChat can deliver more efficient responses with LLM/VLM chatbots through 4-bit inference. TinyChat with LLaMA-3-8b on RTX 4090 (2.7x faster than FP16): TinyChat with LLaMA-3-8b on ...