chandra-ocr-2-GGUF
Chandra-OCR-2 from Datalab is a state-of-the-art OCR model that outputs structured markdown, HTML, or JSON while preserving precise layout information from images and PDFs across 90+ languages. It achieves SOTA benchmarks with 85.9% on olmocr and 77.8% multilingual score (+12% over Chandra 1), delivering major gains in math equation parsing, complex table reconstruction (including merged cells), handwriting recognition, form elements like checkboxes, and wide-document layouts alongside vastly improved image captioning and diagram extraction. Available via free playground, hosted API for production speed/accuracy, or local deployment through HuggingFace Transformers/vLLM, it excels at transforming challenging real-world documents—financial filings, research papers, historical scans, multilingual forms—into semantically rich structured data for downstream AI pipelines and automation workflows.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| chandra-ocr-2-Q2_K.gguf | Q2_K | 2.12 GB | Download |
| chandra-ocr-2-Q3_K_L.gguf | Q3_K_L | 2.69 GB | Download |
| chandra-ocr-2-Q3_K_M.gguf | Q3_K_M | 2.54 GB | Download |
| chandra-ocr-2-Q3_K_S.gguf | Q3_K_S | 2.34 GB | Download |
| chandra-ocr-2-Q4_K_M.gguf | Q4_K_M | 3.07 GB | Download |
| chandra-ocr-2-Q4_K_S.gguf | Q4_K_S | 2.92 GB | Download |
| chandra-ocr-2-Q5_K_M.gguf | Q5_K_M | 3.51 GB | Download |
| chandra-ocr-2.BF16.gguf | BF16 | 9.7 GB | Download |
| chandra-ocr-2.F16.gguf | F16 | 9.7 GB | Download |
| chandra-ocr-2.F32.gguf | F32 | 19.4 GB | Download |
| chandra-ocr-2.Q8_0.gguf | Q8_0 | 5.16 GB | Download |
| chandra-ocr-2.mmproj-bf16.gguf | mmproj-bf16 | 676 MB | Download |
| chandra-ocr-2.mmproj-f16.gguf | mmproj-f16 | 676 MB | Download |
| chandra-ocr-2.mmproj-f32.gguf | mmproj-f32 | 1.33 GB | Download |
| chandra-ocr-2.mmproj-q8_0.gguf | mmproj-q8_0 | 367 MB | Download |
Quants Usage
(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):
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Model tree for prithivMLmods/chandra-ocr-2-GGUF
Base model
datalab-to/chandra-ocr-2