Instructions to use ewin-reg/MiniCPM5-Vision-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ewin-reg/MiniCPM5-Vision-2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ewin-reg/MiniCPM5-Vision-2B", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ewin-reg/MiniCPM5-Vision-2B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ewin-reg/MiniCPM5-Vision-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ewin-reg/MiniCPM5-Vision-2B"
# 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-Vision-2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ewin-reg/MiniCPM5-Vision-2B

SGLang

How to use ewin-reg/MiniCPM5-Vision-2B 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-Vision-2B" \
    --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-Vision-2B",
		"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-Vision-2B" \
        --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-Vision-2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ewin-reg/MiniCPM5-Vision-2B with Docker Model Runner:
```
docker model run hf.co/ewin-reg/MiniCPM5-Vision-2B
```

MiniCPM5-Vision-2B / __init__.py

ewin-reg

feat: release MiniCPM5-V-2B remote code, config, and preprocessors

7997a0c verified 3 days ago

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522 Bytes

	# -- coding: utf-8 --
	from .configuration_minicpm5_v import MiniCPM5VConfig, MiniCPM5VSliceConfig
	from .image_processing_minicpm5_v import MiniCPM5VImageProcessor
	from .modeling_minicpm5_v import (
	MiniCPM5VPreTrainedModel,
	MiniCPM5VForConditionalGeneration,
	MiniCPM5VSpatialUnshuffleProjector
	)

	__all__ = [
	"MiniCPM5VConfig",
	"MiniCPM5VSliceConfig",
	"MiniCPM5VImageProcessor",
	"MiniCPM5VPreTrainedModel",
	"MiniCPM5VForConditionalGeneration",
	"MiniCPM5VSpatialUnshuffleProjector",
	]