AI Dataset2026
Provides 12.26M synthetically generated multilingual OCR samples (en/ja/ko/ru/zh) with word/line/paragraph bounding boxes and reading-order graphs, packaged as HDF5 shards for training detection, recognition, and layout models; licensed CC BY 4.0.
AI Dataset2026
Benchmarks document-parsing systems on real-world enterprise PDFs and images—evaluates tables, charts, content faithfulness, semantic formatting, and visual grounding with human-verified, rule-level tests. Ships with ~2,000 pages, ~169K test rules, and an open evaluation framework for end-to-end pipeline scoring.
AI Dataset2026
Provides a ~9.2M-instance Japanese multimodal post-training dataset for vision–language models, combining image–text pairs, PDF corpora and generated VQA to improve Japanese VLM performance; access is restricted by Japanese copyright (download via llm-jp GitLab).
AI Model2026
Unified multimodal LLM for enterprise workflows: ingests video, audio, image and text to perform transcription, OCR, Q&A, summarization and long-context reasoning. Provides BF16/FP8/NVFP4 weights and integrations with vLLM, TensorRT-LLM and other runtimes.
AI Dataset2026
Provides a 30K+ problem multimodal, multilingual dataset of Olympiad-level math problems with expert solutions and a math-aware retrieval benchmark—includes images, hierarchical topics, provenance from official booklets, and LLM-assisted metadata (v0, CC BY 4.0).
AI Model2026
Unifies video, audio, image and text understanding for enterprise Q&A, summarization, transcription and document intelligence. The NVFP4 quantized variant reduces footprint to ~20.9GB for more efficient single‑GPU deployment and is tuned for NVIDIA runtimes (vLLM, TensorRT).
AI Dataset2026
Provides ~55K multimodal VQA items with matched contrastive pairs and model‑generated rationales across five categories (General, Reasoning, Math, Graph/Chart, OCR), enabling research on faithful visual reasoning and robustness. Train split: 54,844 examples; license unspecified—verify before use.
AI Model2026
Provides a GGUF-quantized build of NVIDIA's Nemotron 3 Nano Omni 30B (Reasoning) for local inference — enables multimodal (video/audio/image/text) reasoning, transcription, and document understanding on compatible runtimes such as llama.cpp, Ollama, vLLM, and TensorRT-LLM.
AI Dataset2026
Collection of 76 image-centric multimodal subdatasets (≈6.9M samples, ~39.56B estimated tokens) for training vision–language models, each published with a standardized conversation JSONL and dataset card. Media are referenced by path/URL and must be fetched separately; licensing is primarily CC-BY-4.0 with per-subdataset variations.
AI Model2026
Unified 4B vision-language model for document understanding that converts images or text into template-driven structured JSON or clean Markdown. Key features: multimodal inputs (image+text), template-based extraction, reasoning vs non-reasoning modes, and vLLM/OpenAI-compatible deployment for OCR, invoice/forms extraction, and RAG preprocessing.
AI Dataset2026
Provides ~85K contrastive visual question–answer pairs where each example contains an anchor and a matched counterpart (image, question, answer). Pairs span General, Reasoning, Math, Graph/Chart and OCR categories to help train and evaluate fine‑grained, faithful visual reasoning in VLMs.
AI Dataset2026
Provides a county-harmonized corpus of U.S. municipal and county ordinance text (≈2.21M chunks) labeled for function, substantive indicator, and topic to support legal NLP, retrieval, and comparative local-law research. Includes model-assigned labels and continuous scorers (opacity, paternalism, enforcement discretion) plus coverage metadata; not exhaustive or a substitute for legal advice.