Instructions to use BAAI/AREX-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BAAI/AREX-2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="BAAI/AREX-2")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("BAAI/AREX-2")
model = AutoModelForMultimodalLM.from_pretrained("BAAI/AREX-2", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BAAI/AREX-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BAAI/AREX-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/AREX-2",
		"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/BAAI/AREX-2

SGLang

How to use BAAI/AREX-2 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 "BAAI/AREX-2" \
    --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": "BAAI/AREX-2",
		"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 "BAAI/AREX-2" \
        --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": "BAAI/AREX-2",
		"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 BAAI/AREX-2 with Docker Model Runner:
```
docker model run hf.co/BAAI/AREX-2
```

Where is the MTP?

by suitup91 - opened 2 days ago

Discussion

suitup91

2 days ago

Should I use the existing qwen 3.8 27b MTP?

ameligrana

1 day ago

•

edited 1 day ago

Seems to work fine using https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/blob/main/MTP/mtp-Qwen3.8-27B-Q4_0.gguf, I see double token generation speed with --spec-type draft-mtp --spec-draft-n-max 3

NikiKrutan

1 day ago

Problem with external MTP head is size.
External: ~1.37 Gb of precious VRAM.
Embedded: ~250-300 Mb.
So MTP should go as a part of a model rather than external.

HermiHg

about 18 hours ago

Problem with external MTP head is size.
External: ~1.37 Gb of precious VRAM.
Embedded: ~250-300 Mb.

The MTP head is a fixed size whether it is bundled or a separate GGUF sidecar. You don't save 1GB just by using the included MTP. For example, I made an external DFlash2 drafter that is only 561MB, which is smaller than most MTP heads.

NikiKrutan

about 10 hours ago

•

edited about 10 hours ago

Problem with external MTP head is size.
External: ~1.37 Gb of precious VRAM.
Embedded: ~250-300 Mb.

The MTP head is a fixed size whether it is bundled or a separate GGUF sidecar. You don't save 1GB just by using the included MTP. For example, I made an external DFlash2 drafter that is only 561MB, which is smaller than most MTP heads.

Well, MTP head in my GGUF quants for Qwen3.8-27B is ~250-300 Mb. IQ4_XS quants. Somehow Unsloth gives Q4_0 (nearly same as IQ4_XS) standalone MTP that is 1.37 Gb. How is that possible?

UPD: Ah, now I see. Standalone MTP includes heavy embedding and output tensors. So not the same as using MTP as a part of a model, where it shares these tensors.

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