Instructions to use Alibaba-DAMO-Academy/RynnBrain-Nav-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alibaba-DAMO-Academy/RynnBrain-Nav-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Alibaba-DAMO-Academy/RynnBrain-Nav-8B", 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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Alibaba-DAMO-Academy/RynnBrain-Nav-8B", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("Alibaba-DAMO-Academy/RynnBrain-Nav-8B", 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?"}
        ]
    },
]
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 Alibaba-DAMO-Academy/RynnBrain-Nav-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alibaba-DAMO-Academy/RynnBrain-Nav-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alibaba-DAMO-Academy/RynnBrain-Nav-8B",
		"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/Alibaba-DAMO-Academy/RynnBrain-Nav-8B

SGLang

How to use Alibaba-DAMO-Academy/RynnBrain-Nav-8B 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 "Alibaba-DAMO-Academy/RynnBrain-Nav-8B" \
    --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": "Alibaba-DAMO-Academy/RynnBrain-Nav-8B",
		"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 "Alibaba-DAMO-Academy/RynnBrain-Nav-8B" \
        --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": "Alibaba-DAMO-Academy/RynnBrain-Nav-8B",
		"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 Alibaba-DAMO-Academy/RynnBrain-Nav-8B with Docker Model Runner:
```
docker model run hf.co/Alibaba-DAMO-Academy/RynnBrain-Nav-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RynnBrain: Open Embodied Foundation Models

If you like our project, please give us a star ⭐ on Github for the latest update.

📰 News

[2026.02.02] Release RynnBrain family weights and inference code.
[2026.02.02] Add cookbooks for cognition, localization, reasoning, and planning.

✨ Introduction

RynnBrain aims to serve as a physics-aware embodied brain: it observes egocentric scenes, grounds language to physical space and time, and supports downstream robotic systems with reliable localization and planning outputs.

Key Highlights

Comprehensive egocentric understanding
Strong spatial comprehension and egocentric cognition across embodied QA, counting, OCR, and fine-grained video understanding.
Diverse spatiotemporal localization
Locates objects, target areas, and predicts trajectories across long episodic context, enabling global spatial awareness.
Physical-space grounded reasoning (RynnBrain family)
The broader RynnBrain family includes “Thinking” variants that interleave textual reasoning with spatial grounding to anchor reasoning in reality.
Physics-aware precise planning (RynnBrain family)
Integrates localized affordances/areas/objects into planning outputs to provide downstream VLA models with precise instructions.

🌎 Model Zoo

Model	Base Model	Huggingface	Modelscope
RynnBrain-2B	Qwen3-VL-2B-Instruct	Link	Link
RynnBrain-4B	Qwen3-VL-4B-Instruct	Link	Link
RynnBrain-8B	Qwen3-VL-8B-Instruct	Link	Link
RynnBrain-30B-A3B	Qwen3-VL-30B-A3B-Instruct	Link	Link
RynnBrain‑CoP-8B	RynnBrain-8B	Link	Link
RynnBrain‑Plan-8B	RynnBrain-8B	Link	Link
RynnBrain‑Plan-30B-A3B	RynnBrain-30B-A3B	Link	Link
RynnBrain‑Nav-8B (This Checkpoint)	RynnBrain-8B	Link	Link

🚀 Main Results

🤖 Quick Start

Minimal dependencies:

pip install transformers==4.57.1

Run text generation:

from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("")
...

Cookbooks

Checkout the cookbooks that showcase RynnBrain's capabilities in cognition, localization, reasoning, and planning.

Category	Cookbook name	Description
Planning	11_visual_language_navigation.ipynb	Combines vision and language instructions to perform navigation and path planning.

📑 Citation

If you find RynnBrain useful for your research and applications, please cite using this BibTeX:

@article{damo2026rynnbrain,
  title={RynnBrain: Open Embodied Foundation Models},
  author={Ronghao Dang, Jiayan Guo, Bohan Hou, Sicong Leng, Kehan Li, Xin Li, Jiangpin Liu, Yunxuan Mao, Zhikai Wang, Yuqian Yuan, Minghao Zhu, Xiao Lin, Yang Bai, Qian Jiang, Yaxi Zhao, Minghua Zeng, Junlong Gao, Yuming Jiang, Jun Cen, Siteng Huang, Liuyi Wang, Wenqiao Zhang, Chengju Liu, Jianfei Yang, Shijian Lu, Deli Zhao},
  journal={arXiv preprint arXiv:2602.14979v1},
  year={2026},
  url = {https://arxiv.org/abs/2602.14979v1}
}

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Safetensors

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Model tree for Alibaba-DAMO-Academy/RynnBrain-Nav-8B

Quantizations

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Collection including Alibaba-DAMO-Academy/RynnBrain-Nav-8B

RynnBrain

Collection

11 items • Updated Apr 13 • 24

Paper for Alibaba-DAMO-Academy/RynnBrain-Nav-8B

RynnBrain: Open Embodied Foundation Models

Paper • 2602.14979 • Published Feb 13 • 46