Image-Text-to-Text
Transformers
English
vision-language-model
multimodal
minicpm5
siglip2
unofficial
experimental
Instructions to use ewin-reg/MiniCPM5-V-1B-unofficial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ewin-reg/MiniCPM5-V-1B-unofficial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ewin-reg/MiniCPM5-V-1B-unofficial")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ewin-reg/MiniCPM5-V-1B-unofficial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ewin-reg/MiniCPM5-V-1B-unofficial with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ewin-reg/MiniCPM5-V-1B-unofficial" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ewin-reg/MiniCPM5-V-1B-unofficial", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ewin-reg/MiniCPM5-V-1B-unofficial
- SGLang
How to use ewin-reg/MiniCPM5-V-1B-unofficial 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 "ewin-reg/MiniCPM5-V-1B-unofficial" \ --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": "ewin-reg/MiniCPM5-V-1B-unofficial", "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 "ewin-reg/MiniCPM5-V-1B-unofficial" \ --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": "ewin-reg/MiniCPM5-V-1B-unofficial", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ewin-reg/MiniCPM5-V-1B-unofficial with Docker Model Runner:
docker model run hf.co/ewin-reg/MiniCPM5-V-1B-unofficial
Download projector.safetensors from ewin-reg/MiniCPM5-V-1B-unofficial: direct link, hf CLI and curl.
- Browser
- Download file 75.5 MB
-
https://huggingface.co/ewin-reg/MiniCPM5-V-1B-unofficial/resolve/7662f2822837e6d0809c995b09b09fba7555bdce/projector.safetensors
- Command line
-
hf download hf://ewin-reg/MiniCPM5-V-1B-unofficial@7662f2822837e6d0809c995b09b09fba7555bdce/projector.safetensors
-
curl -L -o projector.safetensors https://huggingface.co/ewin-reg/MiniCPM5-V-1B-unofficial/resolve/7662f2822837e6d0809c995b09b09fba7555bdce/projector.safetensors
75.5 MB
- Xet hash:
- 344e368dabc48ff190053663dad1c342f2ec4e5694e4cf50eba8cc9676f29fb9
- Size of remote file:
- 75.5 MB
- SHA256:
- e3c2928a989051d736e990d6b72d5d458cbae3116f3ad100a252fafbc6e5e7c0
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