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
```

ewin-reg commited on 3 days ago

Commit

532a4d3

verified ·

1 Parent(s): 6baf351

fix: decode outputs[0] directly — generate() returns only new tokens, not full sequence

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ tags:
   - table-extraction
 ---
-# MiniCPM5-Vision-2B (Unofficial): High-Resolution Vision, Dense OCR & Table Foundation Model
 MiniCPM5-Vision-2B is an omni-modal vision-language foundation model built on the dense language backbone [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) (2.0 billion parameters, 131,072 token context length) coupled to a SigLIP vision backbone (`google/siglip-so400m-patch14-384`) through a learned 2x2 spatial unshuffle projection bridge.
@@ -146,7 +146,7 @@ with torch.no_grad():
         pad_token_id=tokenizer.eos_token_id
     )
-response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
 print(response)
 ```

   - table-extraction
 ---
+# MiniCPM5-Vision-2B: High-Resolution Vision, Dense OCR & Table Foundation Model
 MiniCPM5-Vision-2B is an omni-modal vision-language foundation model built on the dense language backbone [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) (2.0 billion parameters, 131,072 token context length) coupled to a SigLIP vision backbone (`google/siglip-so400m-patch14-384`) through a learned 2x2 spatial unshuffle projection bridge.
         pad_token_id=tokenizer.eos_token_id
     )
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(response)
 ```