Instructions to use openbmb/MiniCPM-V-4_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM-V-4_5 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", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/MiniCPM-V-4_5", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM-V-4_5 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"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4_5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM-V-4_5

SGLang

How to use openbmb/MiniCPM-V-4_5 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" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4_5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4_5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use openbmb/MiniCPM-V-4_5 with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM-V-4_5
```

tc-mb commited on Sep 8, 2025

Commit

d7bbb09

verified ·

1 Parent(s): a18bbd9

Update: README

Browse files

Files changed (1) hide show

README.md +3 -6

README.md CHANGED Viewed

@@ -7,14 +7,11 @@ language:
 - multilingual
 tags:
 - minicpm-v
-- VLM
 - vision
 - ocr
-- document parsing
 - multi-image
 - video
 - custom_code
 ---
 <h1>A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone</h1>
@@ -289,7 +286,7 @@ for new_text in answer:
     print(new_text, flush=True, end='')
 # Second round chat, pass history context of multi-turn conversation
-msgs.append({"role": "assistant", "content": [answer]})
 msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})
 answer = model.chat(
@@ -406,7 +403,7 @@ def encode_video(video_path, choose_fps=3, force_packing=None):
 video_path="video_test.mp4"
 fps = 5 # fps for video
-force_packing = None # You can set force_packing to ensure that 3D-Resampler packing is forcibly enabled; otherwise, encode_video will dynamically set the packing quantity based on the duration.
 frames, frame_ts_id_group = encode_video(video_path, fps, force_packing=force_packing)
 question = "Describe the video"
@@ -418,7 +415,7 @@ msgs = [
 answer = model.chat(
     msgs=msgs,
     tokenizer=tokenizer,
-    use_image_id=False, # ensure use_image_id=False when video inference
     max_slice_nums=1,
     temporal_ids=frame_ts_id_group
 )

 - multilingual
 tags:
 - minicpm-v
 - vision
 - ocr
 - multi-image
 - video
 - custom_code
 ---
 <h1>A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone</h1>
     print(new_text, flush=True, end='')
 # Second round chat, pass history context of multi-turn conversation
+msgs.append({"role": "assistant", "content": [generated_text]})
 msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})
 answer = model.chat(
 video_path="video_test.mp4"
 fps = 5 # fps for video
+force_packing = None # You can set force_packing to ensure that 3D packing is forcibly enabled; otherwise, encode_video will dynamically set the packing quantity based on the duration.
 frames, frame_ts_id_group = encode_video(video_path, fps, force_packing=force_packing)
 question = "Describe the video"
 answer = model.chat(
     msgs=msgs,
     tokenizer=tokenizer,
+    use_image_id=False,
     max_slice_nums=1,
     temporal_ids=frame_ts_id_group
 )