github.com2024-01-12authorityabout the surya familyHN 11 pts · 1 comments3,112 words
Surya is a 650M parameter OCR model that performs layout analysis and table recognition in over 90 languages. The model scores 87.2% on an internal benchmark set of 91 languages and achieves a throughput of 5 pages per second on an RTX 5090. The project code is licensed under Apache 2.0, while the model weights use a modified AI Pubs Open Rail-M license.written, reviewed, cited
- The model weights require a modified AI Pubs Open Rail-M license for startups under $5M funding or revenue.
- Running multiple commands without --keep_server incurs startup and model-load costs for each invocation.
- Layout blocks include a count field estimating token requirements to size the per-block decode budget.
github.com2025-09-01authorityabout the surya familyHN 1 pts · 0 comments895 words
Surya is a 366M-parameter foundation model for heliophysics trained on full-resolution multi-instrument SDO observations across 13 channels from AIA and HMI instruments. The model operates at native 4096×4096 pixel resolution with a 12-minute cadence and was trained on approximately 257TB of processed data spanning 2011 to 2019. It is available under the Apache License 2.0 and supports zero-shot inference for tasks including solar flare forecasting, active region segmentation, solar wind prediction, and EUV spectra modeling.written, reviewed, cited
- The model supports inference on Apple Silicon via the MPS backend alongside CUDA and CPU.
- Training Phase 1 required 160k steps executed across 128 GPUs to achieve one-step ahead forecasting.
- The architecture includes 8 layers of Long-Short Attention Blocks designed for global correlation learning.