Open-Weight Models Under Open RAIL License

Model · Image and text to text

chandra-ocr-2

Datalab

Chandra 2 is a state of the art OCR model from Datalab that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information. Try Chandra in the free playground, or use the hosted API for higher accuracy and speed. - 85.8% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1) - Significant improvements to math, tables, complex layouts - 90+ language support with major accuracy gains - Convert documents to markdown, HTML, or JSON with detailed layout information - Reconstructs forms accurately, including checkboxes - Strong performance with tables, math, and complex layouts - Extracts…

Model · Image and text to text

surya-ocr-2

Datalab

Surya is a 650M param OCR model with these features: - Accuracy - scores 83.3% on olmOCR-bench (top under 3B params) - Multilingual - scores 87.2% on an internal benchmark set of 91 languages (more here) - Layout analysis (table, image, header, etc.) with reading order - Table recognition (rows + columns) It works on a range of documents (see usage and benchmarks). Our managed platform runs both Surya, and variants of our highest accuracy model, Chandra. Get started with $5 in free credits — sign up (takes under 30 seconds) or try our free public playground. Surya is named for the Hindu sun god, who has universal vision. The Surya code is licensed under Apache 2.0. The model weights use a…

Model · Image and text to text

surya-ocr-2-gguf

Datalab

Surya is a 650M param OCR model with these features: - Accuracy - scores 83.3% on olmOCR-bench (top under 3B params) - Multilingual - scores 87.2% on an internal benchmark set of 91 languages (more here) - Layout analysis (table, image, header, etc.) with reading order - Table recognition (rows + columns) It works on a range of documents (see usage and benchmarks). Our managed platform runs both Surya, and variants of our highest accuracy model, Chandra. Get started with $5 in free credits — sign up (takes under 30 seconds) or try our free public playground. Surya is named for the Hindu sun god, who has universal vision. The Surya code is licensed under Apache 2.0. The model weights use a…

Model · Object detection

surya_layout2

Datalab

A lightweight document layout detection model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU. This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model. Documentation, installation, and everything else lives in the Point the fast layout predictor at this checkpoint: Or make it the default so the CLI and library use it without an explicit path: Released under the AI Pubs OpenRAIL-M license (see LICENSE) — the same license as the surya-ocr-2 model weights.

Model · Audio classification

whisper-large-v3-msp-podcast-emotion-dim

Tiantian Feng

This model includes the implementation of dimensional emotion classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits (https://arxiv.org/pdf/2505.14648) The training pipeline used is also the top-performing solution (SAILER) in INTERSPEECH 2025—Speech Emotion Challenge (https://lab-msp.com/MSP-PodcastCompetition/IS2025/). Note that we did not use the transcript compared to our official challenge submission system, and we created a speech-only system to make the model simple but still effective. We use the MSP-Podcast data to train this model, noting that the model might be sensitive to content information when making…

Model · Audio classification

whisper-large-v3-msp-podcast-emotion

Tiantian Feng

This model includes the implementation of categorical emotion classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits (https://arxiv.org/pdf/2505.14648) The training pipeline used is also the top-performing solution (SAILER) in INTERSPEECH 2025—Speech Emotion Challenge (https://lab-msp.com/MSP-PodcastCompetition/IS2025/). Note that we did not use all the augmentation and did not use the transcript compared to our official challenge submission system, but we created a speech-only system to make the model simple but still effective. We use the MSP-Podcast data to train this model, noting that the model might be…

LoRA Flux.1-dev pour le personnage Emma Vaganova, entraînée avec le token 3mm@. Téléchargement: onglet Files and versions de ce dépôt. Utiliser 3mm@ dans le prompt (souvent en fin de phrase: Style of 3mm@). - Résolution: 896×1024 ou buckets proches (aligné sur l’entraînement multi-résolution). Les dossiers d’état d’entraînement (-state/) ne sont pas publiés ici pour l’instant: reprise Comfy = usage local uniquement. Personnage fictif inspiré de références photo; usage responsable et conforme aux conditions FLUX / OpenRAIL. Voir MODELCARD.md (steps, reprise, hyperparamètres, SHA256, limites connues).