7 papers
PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training
Zelun Zhang, Hongen Liu, Suyin Liang +12
We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. Although PaddleOCR-VL-1.5 establishes a strong 0.9B baseline, its remaining e…
Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing
Cheng Cui, Ting Sun, Suyin Liang +15
Document parsing is a fine-grained task where image resolution significantly impacts performance. While advanced research leveraging vision-language models benefits from high-resol…
PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
Cheng Cui, Ting Sun, Suyin Liang +12
We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-worl…
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…