Libraries Transformers How to use openbmb/MiniCPM-OCR-1.0-DSpark with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="openbmb/MiniCPM-OCR-1.0-DSpark") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-OCR-1.0-DSpark")
model = AutoModel.from_pretrained("openbmb/MiniCPM-OCR-1.0-DSpark", device_map="auto") MiniCPM-OCR How to use openbmb/MiniCPM-OCR-1.0-DSpark with MiniCPM-OCR:
pip install "minicpm-ocr[all]" huggingface_hub
# Deploy openbmb/MiniCPM-OCR-1.0-DSpark as a recognition service before running the Python example:
# https://github.com/OpenBMB/MiniCPM-OCR/blob/main/docs/vllm_deploy_en.md
# vLLM: load the model above; use --port 8080,
# --served-model-name minicpm-ocr and --max-model-len 40960.
# Layout detection uses cuda:0; recognition runs on the deployed service. from importlib.resources import files
from pathlib import Path
from minicpmvocr import load_config, pipelined_run
from huggingface_hub import snapshot_download
layout_model = snapshot_download("PaddlePaddle/PP-DocLayoutV3")
cfg = load_config(
str(files("minicpmvocr").joinpath("configs/minicpmvocr.yaml")),
overrides={
"input": {"file_path": str(Path("demo.png").resolve())},
"engine": {
"type": "vllm",
"server_url": "http://127.0.0.1:8080",
"model_name": "minicpm-ocr",
"max_model_len": 40960,
},
"layout": {"model_dir": layout_model, "device": "cuda:0"},
# Render PDFs in-process so this example also works without a main guard.
"pipeline": {"overlap_layout": True, "render_workers": 1},
},
)
pipelined_run(cfg) Notebooks Google Colab Kaggle Local Apps Settings vLLM How to use openbmb/MiniCPM-OCR-1.0-DSpark 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-DSpark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "openbmb/MiniCPM-OCR-1.0-DSpark",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Use Docker docker model run hf.co/openbmb/MiniCPM-OCR-1.0-DSpark SGLang How to use openbmb/MiniCPM-OCR-1.0-DSpark 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-DSpark" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "openbmb/MiniCPM-OCR-1.0-DSpark",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' 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-DSpark" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "openbmb/MiniCPM-OCR-1.0-DSpark",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Docker Model Runner How to use openbmb/MiniCPM-OCR-1.0-DSpark with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM-OCR-1.0-DSpark 
