Image-Text-to-Text
Transformers
Safetensors
multilingual
minicpm-v
vision
ocr
multi-image
video
custom_code
8-bit precision
Instructions to use openbmb/MiniCPM-V-4_5-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-V-4_5-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="openbmb/MiniCPM-V-4_5-int4", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-V-4_5-int4", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use openbmb/MiniCPM-V-4_5-int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/MiniCPM-V-4_5-int4" # 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-V-4_5-int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openbmb/MiniCPM-V-4_5-int4
- SGLang
How to use openbmb/MiniCPM-V-4_5-int4 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-V-4_5-int4" \ --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-V-4_5-int4", "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-V-4_5-int4" \ --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-V-4_5-int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openbmb/MiniCPM-V-4_5-int4 with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM-V-4_5-int4
- Xet hash:
- 7aa36c02e3732946867f7b056ced502124dc2b16093c957386f77b1603a26eca
- Size of remote file:
- 714 Bytes
- SHA256:
- 5c0dbc92920ded2304e2ed6ad4251da56029aa1f3f69427117ac6df9bc68496e
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