Instructions to use ACE-Brain/ACE-Brain-0-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ACE-Brain/ACE-Brain-0-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="ACE-Brain/ACE-Brain-0-8B")
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("ACE-Brain/ACE-Brain-0-8B")
model = AutoModelForImageTextToText.from_pretrained("ACE-Brain/ACE-Brain-0-8B")
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 ACE-Brain/ACE-Brain-0-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ACE-Brain/ACE-Brain-0-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ACE-Brain/ACE-Brain-0-8B",
		"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/ACE-Brain/ACE-Brain-0-8B

SGLang

How to use ACE-Brain/ACE-Brain-0-8B 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 "ACE-Brain/ACE-Brain-0-8B" \
    --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": "ACE-Brain/ACE-Brain-0-8B",
		"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 "ACE-Brain/ACE-Brain-0-8B" \
        --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": "ACE-Brain/ACE-Brain-0-8B",
		"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 ACE-Brain/ACE-Brain-0-8B with Docker Model Runner:
```
docker model run hf.co/ACE-Brain/ACE-Brain-0-8B
```

ACE-Brain commited on Mar 3

Commit

8fbcd0a

verified ·

1 Parent(s): f57184f

update readme

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README.md +2 -3
assets/table1.png +2 -2
assets/table2.png +2 -2
assets/table3.png +2 -2
assets/table4.png +2 -2

README.md CHANGED Viewed

@@ -26,7 +26,7 @@ license: mit
 ## Overview
-**ACE-Brain** is a spatial-centric multimodal foundation model designed to unify perception, reasoning, and decision-making across diverse embodied domains, including **spatial cognition**, **autonomous driving**, **low-altitude sensing** and **embodied interaction**. Built upon a unified multimodal large language model (MLLM) architecture, ACE-Brain learns a shared spatial reasoning substrate that enables generalization across heterogeneous physical environments and agent embodiments.
 Extensive evaluation across **24** benchmarks demonstrates that ACE-Brain achieves state-of-the-art or competitive performance across multiple domains, validating its effectiveness as a unified embodied intelligence model.
@@ -45,8 +45,7 @@ Extensive evaluation across **24** benchmarks demonstrates that ACE-Brain achiev
   - Autonomous Driving
   - Low-Altitude Sensing
   - Embodied Interaction
-- Cross-domain generalization across perception, reasoning, and planning
-- Evaluated on 24 real-world embodied intelligence benchmarks
 ## Performance Highlights

 ## Overview
+**ACE-Brain-0** is a generalist multimodal foundation model designed to unify perception, reasoning, and decision-making across diverse embodied domains, including **spatial cognition**, **autonomous driving**, **low-altitude sensing** and **embodied interaction**. Built upon a unified multimodal large language model (MLLM) architecture, ACE-Brain learns a shared spatial reasoning substrate that enables generalization across heterogeneous physical environments and agent embodiments.
 Extensive evaluation across **24** benchmarks demonstrates that ACE-Brain achieves state-of-the-art or competitive performance across multiple domains, validating its effectiveness as a unified embodied intelligence model.
   - Autonomous Driving
   - Low-Altitude Sensing
   - Embodied Interaction
+- Cross-domain generalization across perception, reasoning, and planning
 ## Performance Highlights