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
Download processor_config.json from openbmb/MiniCPM-OCR-1.0-AWQ: direct link, hf CLI and curl.
- Browser
- Download file 46 Bytes
-
https://huggingface.co/openbmb/MiniCPM-OCR-1.0-AWQ/resolve/main/processor_config.json
- Command line
-
hf download hf://openbmb/MiniCPM-OCR-1.0-AWQ/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/openbmb/MiniCPM-OCR-1.0-AWQ/resolve/main/processor_config.json
46 Bytes
| { | |
| "processor_class": "MiniCPMOcrProcessor" | |
| } |