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

Libraries

How to use FriendliAI/MiMo-Embodied-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="FriendliAI/MiMo-Embodied-7B")
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("FriendliAI/MiMo-Embodied-7B")
model = AutoModelForImageTextToText.from_pretrained("FriendliAI/MiMo-Embodied-7B")
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 FriendliAI/MiMo-Embodied-7B with vLLM:

Install from pip and serve model

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

SGLang

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

| 🤗 HuggingFace | 📔 Technical Report |

I. Introduction

MiMo-Embodied, a powerful cross-embodied vision-language model that shows state-of-the-art performance in both autonomous driving and embodied AI tasks, the first open-source VLM that integrates these two critical areas, significantly enhancing understanding and reasoning in dynamic physical environments.

II. Model Capabilities

III. Model Details

IV. Evaluation Results

MiMo-Embodied demonstrates superior performance across 17 benchmarks in three key embodied AI capabilities: Task Planning, Affordance Prediction, and Spatial Understanding, significantly surpassing existing open-source embodied VLM models and rivaling closed-source models.

Additionally, MiMo-Embodied excels in 12 autonomous driving benchmarks across three key capabilities: Environmental Perception, Status Prediction, and Driving Planning—significantly outperforming both existing open-source and closed-source VLM models, as well as proprietary VLM models.

Moreover, evaluation on 8 general visual understanding benchmarks confirms that MiMo-Embodied retains and even strengthens its general capabilities, showing that domain-specialized training enhances rather than diminishes overall model proficiency.

Embodied AI Benchmarks

Affordance & Planning

Spatial Understanding

Autonomous Driving Benchmarks

Single-View Image & Multi-View Video

Multi-View Image & Single-View Video

General Visual Understanding Benchmarks

Results marked with * are obtained using our evaluation framework.

V. Case Visualization

Embodied AI

Affordance Prediction

Task Planning

Spatial Understanding

Autonomous Driving

Environmental Perception

Status Prediction

Driving Planning

Real-world Tasks

Embodied Navigation

Embodied Manipulation

VI. Citation

@misc{hao2025mimoembodiedxembodiedfoundationmodel,
      title={MiMo-Embodied: X-Embodied Foundation Model Technical Report}, 
      author={Xiaomi Embodied Intelligence Team},
      year={2025},
      eprint={2511.16518},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2511.16518}, 
}

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Safetensors

Model size

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Tensor type

BF16

F32

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Paper for FriendliAI/MiMo-Embodied-7B

MiMo-Embodied: X-Embodied Foundation Model Technical Report

Paper • 2511.16518 • Published Nov 20, 2025 • 26