Instructions to use XiaomiMiMo/MiMo-VL-7B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiaomiMiMo/MiMo-VL-7B-RL with Transformers:

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

pipe = pipeline("image-text-to-text", model="XiaomiMiMo/MiMo-VL-7B-RL")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("XiaomiMiMo/MiMo-VL-7B-RL")
model = AutoModelForImageTextToText.from_pretrained("XiaomiMiMo/MiMo-VL-7B-RL")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use XiaomiMiMo/MiMo-VL-7B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-VL-7B-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-VL-7B-RL",
		"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/XiaomiMiMo/MiMo-VL-7B-RL

SGLang

How to use XiaomiMiMo/MiMo-VL-7B-RL 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 "XiaomiMiMo/MiMo-VL-7B-RL" \
    --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": "XiaomiMiMo/MiMo-VL-7B-RL",
		"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 "XiaomiMiMo/MiMo-VL-7B-RL" \
        --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": "XiaomiMiMo/MiMo-VL-7B-RL",
		"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 XiaomiMiMo/MiMo-VL-7B-RL with Docker Model Runner:
```
docker model run hf.co/XiaomiMiMo/MiMo-VL-7B-RL
```

where is the mmproj file?

by jeffzhou2000 - opened May 31, 2025

Discussion

jeffzhou2000

May 31, 2025

•

edited May 31, 2025

thanks for your excellent MiMo-VL-7B-RL model.

I build a MiMo-VL-7B-RL-q4_k_m.gguf model in https://huggingface.co/zhouwg/kantv/tree/main by the official tools provided in llama.cpp.

then I compared it with Qwen1.5-1.8B,Qwen2.5-3B,Qwen3-4B,Qwen3-8B,gemma-3-4b,gemma-3-12b,Llama-3.1-Nemotron-Nano-4B,Phi-4-mini-reasoning,DeepSeek-R1-0528-Qwen3-8B on my Qualcomm Snapdragon 8Elite based phone, I can't believe that it achieved the second best overall experience at the moment:

for some questions, the MiMo-VL-7B-RL is much better than DeepSeek-R1-0528-Qwen3-8B and Qwen series, Gemma-3-4B is a little better than MiMo-VL-7B-RL.
the MiMo-VL-7B-RL also gave some stupid answers to some simple questions, DeepSeek-R1-0528-Qwen3-8B is better on these questions, Google's Gemma-3-4B achieved the best overall experience.

my question is:

as an Image-Text-to-Text multimodal model, where is the mmproj model file? should we create the mmproj model file manually? how to create the mmproj model file?

thanks.

jeffzhou2000 changed discussion status to closed May 31, 2025

jeffzhou2000 changed discussion status to open May 31, 2025

tobiaslee

Jun 3, 2025

Hi, please follow the recipe of Qwen25VL for deploying with llama.cpp.

jeffzhou2000

Jun 4, 2025

thanks for you reminder and help, I'll try.

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