PaddlePaddle has released HPD-Parsing, a new lightweight document parsing model that utilizes a Hierarchical Parallel Decoding paradigm. This model achieves a new state-of-the-art score of 94.91% on the OmniDocBench v1.6 benchmark and boasts a peak throughput of 4,752 TPS. The HPD-Parsing model is designed to overcome the sequential bottleneck of traditional parsers by coordinating global document structure and dispatching localized content generation to concurrent branches, significantly improving inference efficiency. AI
IMPACT Sets new SOTA on document parsing benchmarks and significantly increases throughput, potentially accelerating document processing workflows.
RANK_REASON Model release from a known lab (PaddlePaddle) with performance metrics. [lever_c_demoted from frontier_release: ic=2 ai=1.0]
- HPD-Parsing
- Hugging Face
- OmniDocBench
- PaddlePaddle
- Docker
- Hierarchical Parallel Decoding
- OmniDocBench v1.6
- OpenAI
- PaddlePaddle/HPD-Parsing
- SGLang
- transformers
- vLLM
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