opendatalab

3. MinerU, from OpenDataLab. PDFs to Markdown. Apache 2.0 plus conditions. Outside test: equations came back as LaTeX. Telemetry "initially enabled". Prompt (untested): "In a new venv, install mineru, run mineru telemetry disable, parse one PDF." opendatalab/MinerU

"MinerU 4.0 brings document parsing, a local document library, and service tools into one workflow for document conversion, application integration, and agent reading." ➡️ 82K STARS! Have you hear of this? opendatalab/MinerU Community Overview: lifehubber.com Resources are shared for discovery and are not independently vetted—please do your own due diligence. New resources are added regularly — feel free to join the sub for updates. Full searchable archive of all resources posted so far on our community site, LifeHubber: lifehubber.com 300+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

"MinerU 4.0 brings document parsing, a local document library, and service tools into one workflow for document conversion, application integration, and agent reading." ➡️ 82K STARS! Have you hear of this? opendatalab/MinerU Community Overview: lifehubber.com Resources are shared for discovery and are not independently vetted—please do your own due diligence. New resources are added regularly — feel free to join the sub for updates. Full searchable archive of all resources posted so far on our community site, LifeHubber: lifehubber.com 300+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

MinerU：一个把复杂 PDF 和 Office 文档转成 LLM/Agent 可用 Markdown 或 JSON 的开源工具。它面向多页版面、表格、公式和内容块解析，并保留定位信息，适合研究资料、技术文档和合同入库；高质量解析通常需要额外模型与硬件资源，部署前要先核对对应版本的许可证。 👉 opendatalab/MinerU

RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature Screen Tables and Substrate Scopes are information-dense figures widely used in synthetic chemistry literature, yet automated extraction of structured reaction data from them remains challenging. We introduce RxnExBench, a multimodal benchmark for fine-grained reaction-level evaluation. Built from high-impact chemistry journals, it contains 3,565 expert-annotated reactions and 2,361 molecular structures. The dataset adopts a unified schema with 15 reaction roles and 26 core fields, supporting downstream AI-for-Chemistry tasks such as retrosynthetic planning, yield prediction, and reaction condition recommendation. RxnExBench further provides a reasoning-driven difficulty taxonomy, a modular annotation pipeline, and the Oracle-MolRec protocol, which disentangles molecular recognition from higher-level reasoning. Evaluation of 9 state-of-the-art VLMs shows that performance degrades substantially as schema complexity and reasoning demands increase. Under the Oracle-MolRec setting, results further reveal that accurate molecular recognition alone is insufficient, with multimodal integration and chemistry-aware reasoning remaining the primary bottlenecks. We release all data and code to facilitate future research in chemistry-aware multimodal understanding and AI-for-Chemistry dataset construction. The dataset and code will be released at opendatalab/RxnExBench.

@dionisiodev Fala Dionisio, use uma api, recomendo o opendatalab/mineru para subir sua na propria infra se for dado sensivel, ou use a api deles mineru.net para sua integracao. Como e uma coisa simples de 5k a 10k estaria bem pago.

DocLayout-YOLO: Axis-Attentive Document Layout Detector with Mesh-Guided Synthetic Pretraining Accurate and efficient document layout analysis is critical for modern document parsing systems. However, although the YOLO framework represents a leading paradigm for the real-time detector, its adaptation to heterogeneous documents remains underexplored. To address this gap, we propose DocLayout-YOLO, a document-specialized real-time detector that integrates diverse synthetic document pretraining and page-axis context modeling. From the data perspective, we introduce MeshLayout-1M, a synthetic pretraining corpus generated by the proposed Mesh-candidate BestFit algorithm, which provides large-scale and diverse document layouts for pretraining and improves adaptation to heterogeneous document scenarios. From the model perspective, we propose Page-Axis Layout Attention (PALA), which captures long-range dependencies along the horizontal and vertical page axes while remaining computationally efficient. Extensive experiments demonstrate that DocLayout-YOLO achieves state-of-the-art performance on four heterogeneous document benchmarks, obtaining 72.4%/75.5%/96.0%/73.1% mAP50:95 on D4LA, M6Doc, HJDataset, and PRIMA-LAD, respectively. Compared with existing real-time detectors, DocLayout-YOLO establishes a superior accuracy-efficiency trade-off in document layout analysis. Code, model, and data are available at github.com/opendatalab/DocLayout-YOLO/tree/releas….

DocLayout-YOLO ⭐ 2.3k DocLayout-YOLO is an open-source model for real-time document layout detection, built on YOLO-v10, for developers. 🔗 opendatalab/DocLayout-YOLO #opensource t.co

Youtu-Parsing-Omni vs MinerU and PaddleOCR-VL: Is a 5B PDF Parser Worth It? Tencent just open-sourced Youtu-Parsing-Omni, a 5B model that turns PDF page images, tables, charts and scans into structured JSON, and its model card puts it at #1 on OmniDocBench v1.6 with 96.96. The runner-up, TeleOCR, scores 96.91. So the headline win is 0.05 points, and the winner is about four times bigger than the 1.2B model it beat. I still think it's worth your attention if you run a RAG pipeline. It has the best reading-order score in that table and outputs a bounding box for every element, and the same checkpoint handles charts, audio and video too. I went through what Youtu-Parsing-Omni extracts from PDFs, tables and images, how it scores against MinerU, PaddleOCR-VL and Marker, the license clause that rules out the EU, and how to serve it locally with vLLM. What Youtu-Parsing-Omni actually is The model card lists 5B parameters in BF16 (the explainx writeup counts 5.33B and roughly 10.7 GB of weights). One shared "Youtu-Omni-Encoder" feeds a Youtu-LLM decoder. It ships as custom code (tagged youtu_vita), so you need trust_remote_code=True. The weights, a vLLM plugin and examples landed in October 2026, and the code lives in TencentCloudADP/youtu-parsing. The older Youtu-Parsing (2.5B, 93.74 on the same table) only parsed documents. Omni adds images, charts, audio and video, and you pick a mode with --task: - document: layout elements with boxes, text, LaTeX formulas, tables in OTSL, charts as Markdown, flowcharts as Mermaid, plus reading order - graphics_chart: chart to Markdown table - graphics_flowchart: Mermaid code - graphics_geometric: points, lines, shapes, measurements - natural_image, audio, natural_video, textrich_video: captions, ASR with timestamps, OCR per video segment Every task returns one fenced JSON block with modality, subtype, elements (or segments) and global_description. Coordinates are tokens on a 0–1000 grid, like <box><x_85><y_275><x_1000><y_1000></box>. For RAG, the boxes matter more than the benchmark. If every chunk carries a page number and a box, you can show the user the exact region a citation came from. Most text-only parsers throw that away. Youtu-Parsing-Omni vs MinerU, PaddleOCR-VL and Marker These numbers come from Tencent's model card, except the two pipeline rows, which come from the official OmniDocBench leaderboard (v1.6). Overall is ((1 − text edit) × 100 + table TEDS + formula CDM) / 3. | Model | Params | Overall ↑ | Text Edit ↓ | Formula CDM ↑ | Table TEDS ↑ | Reading Order ↓ | |---|---|---|---|---|---|---| | Youtu-Parsing-Omni | 5B | 96.96 | 0.0271 | 96.80 | 96.79 | 0.1104 | | TeleOCR | 1.2B | 96.91 | 0.0267 | 96.59 | 96.82 | 0.1184 | | OvisOCR2 | 0.8B | 96.47 | 0.0265 | 97.49 | 94.58 | 0.1120 | | PaddleOCR-VL-1.6 | 0.9B | 96.34 | 0.0326 | 97.53 | 94.76 | 0.1278 | | MinerU2.5-Pro | 1.2B | 95.75 | 0.0360 | 97.45 | 93.42 | 0.1200 | | Gemini-3-Pro | – | 92.91 | 0.0640 | 95.99 | 89.15 | 0.1650 | | GPT-5.2 | – | 86.59 | 0.1140 | 88.21 | 82.95 | 0.1930 | | MinerU-Pipeline 3.4.0 | – | 86.47 | – | – | – | – | | Marker 1.8.2 | – | 78.44 | – | – | – | – | The top five are basically tied. TeleOCR has better text edit distance and table TEDS. OvisOCR2 and PaddleOCR-VL beat Youtu on formulas while being under 1B parameters. If you parse math-heavy papers, a 0.9B model wins on formulas with under a fifth of the parameters. Where Youtu does lead is reading order. 0.1104 is the best in the table, and its structure-only table score (TEDS-S 98.20) is also top. Bad reading order is what produces chunks that start mid-sentence in column two and finish in a footnote. That's where I'd expect the difference to show up in retrieval. Docling isn't in any of these tables. OmniDocBench lists Docling as a tool but its current v1.6 table doesn't score it, and the model card leaves it out too. If you came here hoping for Youtu vs Docling numbers, I couldn't find any. Marker 1.8.2 does have a score, and 78.44 against 96.96 is a large gap. Keep in mind that Marker can run on a laptop CPU and this model needs a CUDA GPU. Why I don't trust a 0.05-point OmniDocBench lead Youtu-Parsing-Omni is not on the official OmniDocBench leaderboard yet. The older Youtu-Parsing is (93.74), and the history behind that entry is useful. In issue #14, the OmniDocBench team re-ran Youtu-Parsing on v1.5 and got 93.37 overall. Tencent's internal number was 93.22. The overall scores are close, but formula CDM differs by about 2 points (91.22 vs 93.19) and table TEDS by about 2 in the other direction (93.10 vs 91.15). Tencent put the gap down to different testing environments and called it normal variation. A 2-point swing on sub-metrics is far bigger than the 0.05 lead in the headline. Treat the leaderboard order as noise until someone re-runs it independently. The OmniParsingBench numbers have the same caveat. Youtu averages 75.08 against Gemini-3-Pro's 77.44, wins on charts (95.02) and audio (78.08), and loses clearly on natural images (62.84 vs 69.73) and geometry (74.33 vs 85.43). The benchmark comes from the Logics-Parsing-Omni team, but it shipped without a complete scoring toolkit, so Tencent wrote its own and ran and scored every model itself. Part of the score comes from an LLM judge. There are also no throughput numbers. The repo advertises a 5–11x speedup from "Token Parallelism" for the older model, but issue #12 confirms it isn't integrated into vLLM. If you serve through vLLM, don't count on that speedup. The license excludes the EU: read section 0 first The license is a custom "youtu-parsing" license, not Apache or MIT. The grant is perpetual, worldwide and royalty-free, and the LICENSE file has no MAU threshold. Section 0 says: > Youtu-Parsing IS NOT INTENDED FOR USE WITHIN THE EUROPEAN UNION. It also says that clause prevails in any conflict. If your users or your servers are in the EU, talk to legal before you build on this. PaddleOCR-VL or MinerU might be the boring, safer pick for you. How much VRAM does Youtu-Parsing-Omni need? The card gives no VRAM figure. What it does give is the launch config in scripts/vllm.sh: max-model-len 65536, gpu-memory-utilization 0.70, max-num-seqs 1, bfloat16. With about 10.7 GB of weights, 70% of a 16 GB card is 11.2 GB, which leaves almost nothing for KV cache. My estimate is that a 24 GB card is the realistic floor for the default script. Someone has already opened a GGUF request, and the model tree lists two quantizations (MLX 4-bit and 8-bit, so Apple silicon only), but I haven't seen any quality numbers for them. How to run Youtu-Parsing-Omni for local document OCR The vLLM and Transformers paths pin different Transformers versions (5.2.0 vs 5.10.2), so give each one its own environment: git clone https://github.com/TencentCloudADP/youtu-parsing.git cd youtu-parsing/youtu_parsing_omni # Python >= 3.10, CUDA GPU bash scripts/setup_env.sh # vLLM serving # bash scripts/setup_env.sh --transformers # separate venv for Transformers MODEL=tencent/Youtu-Parsing-Omni DATA_ROOT=/path/to/media GPU=0 bash scripts/vllm.sh That serves an OpenAI-compatible API at http://127.0.0.1:8000/v1 under the model name Youtu-Parsing-Omni. The model reads images, not PDFs, so rasterize first and put the pages under DATA_ROOT: pdftoppm -r 150 -png report.pdf /path/to/media/report/page for p in /path/to/media/report/page-*.png; do python examples/infer_vllm.py --task document --media "$p" done If you call the API yourself, copy the settings from the card's raw example: temperature=0.0, max_tokens=32768, chat_template_kwargs={"enable_thinking": False, "enable_parsing": True}, skip_special_tokens=False, and stop token IDs for <|image_pad|> and <|end_of_text|>. The prompt text for each task is in prompts/youtu_parsing_omni.json. Don't write your own; these parsers are trained on one exact prompt. On the Transformers path, pin a commit with --revision. The card recommends it because of trust_remote_code, and I'd do it anyway. You don't want remote code changing underneath a production ingest job. Using Youtu-Parsing-Omni in a RAG pipeline Here's the setup I'd try: 1. One JSON file per page, stored next to the source PDF. Never re-parse just because you changed the chunker. 2. Chunk on elements in the model's reading order, not on fixed token windows. Keep tables and formulas as whole chunks. 3. Store page, the 0–1000 box and subtype as metadata on each chunk, so citations can highlight the source region. 4. Index the table's Markdown, but keep the OTSL/HTML around for display. 5. Run a 30-page sample from your own worst PDFs through this and through PaddleOCR-VL-1.6 before deciding. On the benchmark they're 0.62 apart. Your scanned invoices won't look like OmniDocBench. My take: if you're on Marker or a plain text extractor and your answers fall apart on multi-column layouts, the jump to any model in that top five will matter much more than which one you pick. Youtu-Parsing-Omni is worth considering if you need boxes plus reading order and also want charts and audio from one model. If you only parse PDFs, the sub-1B models give you almost the same scores for much less VRAM, and their licenses don't exclude the EU. What do you use to turn PDFs into clean text for RAG right now, and what keeps breaking? Tables, two-column papers, scanned forms? I'm especially curious whether anyone has run a VLM parser against Docling on real documents and found it worth the GPU. Sources - tencent/Youtu-Parsing-Omni model card - Youtu-Parsing-Omni LICENSE - TencentCloudADP/youtu-parsing on GitHub - Issue #14: OmniDocBench v1.5 score alignment - Issue #12: Token Parallelism in vLLM - OmniDocBench leaderboard - explainx: Youtu-Parsing-Omni overview

CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence Multimodal Large Language Models (MLLMs) have significantly advanced document understanding, yet current Doc-VQA evaluations score only the final answer and leave the supporting evidence unchecked. This answer-only approach masks a critical failure mode: a model can land on the correct answer while grounding it in the wrong passage---a critical risk in high-stakes domains like law, finance, and medicine, where every conclusion must be traceable to a specific source region. To address this, we introduce CiteVQA, a benchmark that requires models to return \textit{element-level} bounding-box citations alongside each answer, evaluating both jointly. CiteVQA comprises 1,897 questions across 711 PDFs spanning seven domains and two languages, averaging 40.6 pages per document. To ensure fidelity and scalability, the ground-truth citations are generated by an automated pipeline---which identifies crucial evidence via masking ablation and enforces multi-stage quality control. At the core of our evaluation is Strict Attributed Accuracy (SAA), which credits a prediction only when the answer and the cited region are both correct. Auditing 20 MLLMs reveals a pervasive Attribution Hallucination: models frequently produce the right answer while citing the wrong region. The strongest system (Gemini-3.1-Pro-Preview) achieves an SAA of only 76.0, and the strongest open-source MLLM reaches just 22.5. Ultimately, towards trustworthy document intelligence, CiteVQA exposes a reliability gap that answer-only evaluations overlook, providing the instrumentation needed to close it. Our repository is available at opendatalab/CiteVQA.