Popular GitHub Repositories for Efficient Model

[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment

[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

[CVPR'20] ZeroQ: A Novel Zero Shot Quantization Framework

[ICML'21 Oral] I-BERT: Integer-only BERT Quantization

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

[KDD'22] Learned Token Pruning for Transformers

Code for the AAAI 2024 Oral paper "OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models".

[JMLR'20] NeurIPS 2019 MicroNet Challenge Efficient Language Modeling, Champion

[MICCAI 2021] BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation

[ICASSP'22] Integer-only Zero-shot Quantization for Efficient Speech Recognition

GraphSnapShot: Caching Local Structure for Fast Graph Learning [Efficient ML System]

Exploring Variational Deep Q Networks. A study undertaken for the University of Cambridge's R244 Computer Science Masters Course. Inspired by https://arxiv.org/abs/1711.11225/.

NAACL 论文《量化如何影响大型语言模型的置信度？》的代码