Model Detail
chandra-ocr-2
—chandra-ocr-2 is a multimodal model with 2.6B parameters released by datalab-to. The model is registered under the image-text-to-text pipeline tag on Hugging Face, released under the openrail license.
chandra-ocr-2 ships with 2.6B parameters. Total weight footprint is approximately 5.3 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the openrail license — review the exact terms before commercial deployment.
Downloads of chandra-ocr-2 have moved -52.5% over the trailing seven days. The decline is steep, which typically signals a newer release displacing this checkpoint or a known issue surfacing in the community. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
chandra-ocr-2 is best fit for mixed text-and-image reasoning tasks such as document understanding. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning
arXiv:2606.19329v2 Announce Type: replace-cross Abstract: We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use source properties such as magnitudes, colors, and distances to id
Extracting latent representations from X-ray spectra. Classification, regression, and accretion signatures of Chandra sources
arXiv:2510.14102v2 Announce Type: replace-cross Abstract: Spectral signatures are crucial in the era of large X-ray surveys. Automatic machine learning methods have proven useful in this respect, but so far they have not been applied to large spectral datasets, such as the Chandra Source Catalog (CS
