136 citations · 608 across the 25 of their papers we have counts for
5 papers · 1 filter
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model
Cheng Cui, Ting Sun, Suyin Liang +15
In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-l…
GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research
Xinqi Li, Yiqun Liu, Shan Jiang +6
We introduce GraphNet, a dataset of 2.7K real-world deep learning computational graphs with rich metadata, spanning six major task categories across multiple deep learning framewor…
CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs
Zhaojing Zhou, Xunchao Li, Minghao Li +8
The rapid scaling of Large Language Models (LLMs) elevates inference costs and compounds substantial deployment barriers. While quantization to 8 or 4 bits mitigates this, sub-3-bi…
PaddleOCR 3.0 Technical Report
Cheng Cui, Ting Sun, Manhui Lin +16
This technical report introduces PaddleOCR 3.0, an Apache-licensed open-source toolkit for OCR and document parsing. To address the growing demand for document understanding in the…
AdaGC: Enhancing LLM Pretraining Stability via Adaptive Gradient Clipping
Guoxia Wang, Shuai Li, Congliang Chen +5
Loss spikes remain a persistent obstacle in large-scale language model pretraining. While previous research has attempted to identify the root cause of loss spikes by investigating…