Image-Text-to-Text
Safetensors
English
Chinese
minicpm_ocr
minicpm-ocr
ocr
document-parsing
multimodal
awq
4-bit precision
quantized
conversational
Instructions to use openbmb/MiniCPM-OCR-1.0-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use openbmb/MiniCPM-OCR-1.0-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/MiniCPM-OCR-1.0-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM-OCR-1.0-AWQ", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/openbmb/MiniCPM-OCR-1.0-AWQ
- SGLang
How to use openbmb/MiniCPM-OCR-1.0-AWQ with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "openbmb/MiniCPM-OCR-1.0-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM-OCR-1.0-AWQ", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "openbmb/MiniCPM-OCR-1.0-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM-OCR-1.0-AWQ", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use openbmb/MiniCPM-OCR-1.0-AWQ with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM-OCR-1.0-AWQ
Commit History
3 days ago
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Add the MiniCPM logo to the top of the model card
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Align the top navigation with MiniCPM-OCR-1.0
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Point the API link to the official document-parse docs
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Point Ollama to its deploy guide and drop the Transformers link
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Fix SDK doc links from minicpmvocr to minicpm_ocr
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5 days ago
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Restore top navigation and remove the hero image from the model card.
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docs: serve card images from the GitHub repo
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Update the English case examples image on the model card.
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Add top navigation links and remove the hero image from the model card.
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Rename quality-assessment to quality-decision in the model card.
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docs: sync with latest GitHub
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Point documentation links to the public OpenBMB/MiniCPM-OCR repository.
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config: align with main repo (native minicpm_ocr, tf 5.21)
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docs: full model card + quantization notes
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docs: clarify file list
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add model card
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Update processor_config.json
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Update preprocessor_config.json
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Update generation_config.json
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Update config.json
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7 days ago
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init: quantized weights
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initial commit
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