Downloads · 30 days
667
5% of all-time downloads
prithivMLmods/chandra-OCR-GGUF
chandra-OCR-GGUF is a image-text-to-text model from prithivMLmods. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as openrail.
Chandra is a highly accurate OCR model designed to convert images and PDFs into structured outputs such as markdown, HTML, and JSON while preserving detailed layout information. It supports over 40 languages and excel…
Downloads · 30 days
667
5% of all-time downloads
All-time downloads
13.5K
Public
Repo size
97.4 GB
Likes
13
Public
Click a slice to open those files.
.gguf97.4 GB · 100%
From the Hugging Face model README
Chandra is a highly accurate OCR model designed to convert images and PDFs into structured outputs such as markdown, HTML, and JSON while preserving detailed layout information. It supports over 40 languages and excels in handling complex document elements including handwriting, tables, math expressions, forms with checkboxes, and diagrams with captions. Chandra offers flexible inference modes with local execution via HuggingFace or remote deployment using a vLLM server, making it suitable for both interactive use and large-scale batch processing. Its strong layout preservation and multilingual capabilities make it a versatile choice for document digitization and automated content extraction workflows.
| File Name | Quant Type | File Size |
|---|---|---|
| chandra-BF16.gguf | BF16 | 16.4 GB |
| chandra-F16.gguf | F16 | 16.4 GB |
| chandra-F32.gguf | F32 | 32.8 GB |
| chandra-Q3_K_M.gguf | Q3_K_M | 4.12 GB |
| chandra-Q3_K_S.gguf | Q3_K_S | 3.77 GB |
| chandra-Q4_K_M.gguf | Q4_K_M | 5.03 GB |
| chandra-Q4_K_S.gguf | Q4_K_S | 4.8 GB |
| chandra-Q8_0.gguf | Q8_0 | 8.71 GB |
| chandra-mmproj-bf16.gguf | mmproj-bf16 | 1.16 GB |
| chandra-mmproj-f16.gguf | mmproj-f16 | 1.16 GB |
| chandra-mmproj-f32.gguf | mmproj-f32 | 2.31 GB |
| chandra-mmproj-q8_0.gguf | mmproj-q8_0 | 752 MB |
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
