How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/chandra-ocr-2-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/chandra-ocr-2-GGUF", device_map="auto")
Quick Links

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 documentsโ€”financial filings, research papers, historical scans, multilingual formsโ€”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.BF16.gguf BF16 9.7 GB Download
chandra-ocr-2.F16.gguf F16 9.7 GB Download
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_0.gguf Q4_0 2.9 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_0.gguf Q5_0 3.43 GB Download
chandra-ocr-2.Q5_K_M.gguf Q5_K_M 3.51 GB Download
chandra-ocr-2.Q5_K_S.gguf Q5_K_S 3.43 GB Download
chandra-ocr-2.Q6_K.gguf Q6_K 3.99 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-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):

image.png

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