Search Results for author:
Found 10 papers, 4 papers with code
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs
We introduce Integer Scale, a novel post-training quantization scheme for large language models that effectively resolves the inference bottleneck in current fine-grained quantization approaches while maintaining similar accuracies.
UMIE: Unified Multimodal Information Extraction with Instruction Tuning
Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases.
A Speed Odyssey for Deployable Quantization of LLMs
The large language model era urges faster and less costly inference.
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
On GLM-130B and OPT-66B, our method even achieves the same level of accuracy at 2-bit quantization as their float ones.
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
In this study, we propose a novel W4A8 post-training quantization method for the available open-sourced LLMs, which combines the advantages of both two recipes.
EAPruning: Evolutionary Pruning for Vision Transformers and CNNs
In this paper, we undertake a simple and effective approach that can be easily applied to both vision transformers and convolutional neural networks.
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios.
Ranked #14 on
Object Detection
on COCO-O
AutoKWS: Keyword Spotting with Differentiable Architecture Search
Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption.
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
To discover powerful yet compact models is an important goal of neural architecture search.
Ranked #76 on
Neural Architecture Search
on <h2>oi</h2>
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Deep convolutional neural networks demonstrate impressive results in the super-resolution domain.
Ranked #16 on
Image Super-Resolution
on BSD100 - 2x upscaling
