How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/chandra-ocr-2-GGUF:
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/chandra-ocr-2-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/chandra-ocr-2-GGUF:
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/chandra-ocr-2-GGUF:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/chandra-ocr-2-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/chandra-ocr-2-GGUF:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/chandra-ocr-2-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/chandra-ocr-2-GGUF:
Use Docker
docker model run hf.co/prithivMLmods/chandra-ocr-2-GGUF:
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 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.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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GGUF
Model size
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Architecture
qwen35
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