PaddlePaddle/HPD-Parsing · Hugging Face

HPD-Parsing: Hierarchical Parallel Document Parsing We introduce HPD-Parsing, a lightweight (1B) and high-throughput document parsing model built on a Hierarchical Parallel Decoding paradigm. Unified VLM-based parsers process an entire page jointly but generate the output through a single token-by-token autoregressive trajectory, creating a sequential bottleneck that grows with document length. HPD-Parsing is motivated by a key property of document parsing: page structure requires global coordination, whereas content generation is largely localized within individual regions. Based on this observation, a main layout branch coordinates the global document structure and dynamically dispatches localized content generation to concurrent branches, while Progressive Multi-Token Prediction (P-MTP) further reduces the decoding steps within each branch. HPD-Parsing achieves an overall score of 94.91% on OmniDocBench v1.6 — a new state of the art among end-to-end unified parsers — while reaching a peak throughput of 4,752 TPS, 2.62× the fastest existing document parser and 3.06× its own autoregressive baseline. Key Capabilities of HPD-Parsing 🚀 Hierarchical Parallel Decoding for High-Throughput Document Parsing: We introduce Hierarchical Parallel Decoding (HPD), a new decoding paradigm that restructures full-page autoregressive generation into globally coordinated, localized parallel decoding. A main layout branch performs global coordination and dynamically decomposes the conventional single decoding trajectory into concurrent content branches, each responsible for a localized document region. Within each branch, P-MTP further reduces the number of decoding steps by predicting multiple future tokens at each iteration. Together with shared-prefix KV cache reuse, HPD substantially shortens the effective sequential decoding path along both branch and token dimensions. 🔄 Staged Adaptation with Automated Difficulty-Aware Data Curation: We develop a staged adaptation strategy that transfers conventional autoregressive document parsing capabilities to the proposed hierarchical parallel decoding paradigm while preserving parsing accuracy. The strategy is supported by an automated difficulty-aware data curation pipeline that integrates large-scale data collection, model-assisted annotation, difficulty estimation, and balanced sampling. By progressively adapting the model and emphasizing challenging samples, the training framework mitigates the accuracy degradation caused by the transition to parallel decoding with minimal manual annotation effort. ⚡ State-of-the-Art Throughput with Competitive Parsing Accuracy: HPD-Parsing achieves state-of-the-art inference efficiency on OmniDocBench v1.6, reaching a peak throughput of 4,752 Tokens Per Second (TPS). It delivers 1.62× the throughput of the fastest existing document parsing model and more than 3.06× that of its autoregressive baseline, while maintaining competitive parsing accuracy. These results demonstrate that document parsing can be effectively executed through global layout coordination and localized parallel decoding rather than a single sequential generation trajectory. submitted by /u/pmttyji [link] [comments]

Source: r/LocalLLaMA | 2026-07-23