- Libraries
- Transformers
How to use Gabriel/diffu-minicpm-ocr-htr-dspark with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="Gabriel/diffu-minicpm-ocr-htr-dspark", trust_remote_code=True) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Gabriel/diffu-minicpm-ocr-htr-dspark", trust_remote_code=True, device_map="auto") - MiniCPM-OCR
How to use Gabriel/diffu-minicpm-ocr-htr-dspark with MiniCPM-OCR:
pip install "minicpm-ocr[all]" huggingface_hub
# Deploy Gabriel/diffu-minicpm-ocr-htr-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 Gabriel/diffu-minicpm-ocr-htr-dspark with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gabriel/diffu-minicpm-ocr-htr-dspark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Gabriel/diffu-minicpm-ocr-htr-dspark",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/Gabriel/diffu-minicpm-ocr-htr-dspark
- SGLang
How to use Gabriel/diffu-minicpm-ocr-htr-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 "Gabriel/diffu-minicpm-ocr-htr-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": "Gabriel/diffu-minicpm-ocr-htr-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 "Gabriel/diffu-minicpm-ocr-htr-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": "Gabriel/diffu-minicpm-ocr-htr-dspark",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' - Docker Model Runner
How to use Gabriel/diffu-minicpm-ocr-htr-dspark with Docker Model Runner:
docker model run hf.co/Gabriel/diffu-minicpm-ocr-htr-dspark
