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

Libraries

How to use BAAI/AREX-Base with Transformers:

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

pipe = pipeline("text-generation", model="BAAI/AREX-Base")
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-Base")
model = AutoModelForMultimodalLM.from_pretrained("BAAI/AREX-Base", 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-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BAAI/AREX-Base"
# 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-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BAAI/AREX-Base

SGLang

How to use BAAI/AREX-Base 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-Base" \
    --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-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Base" \
        --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-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BAAI/AREX-Base with Docker Model Runner:
```
docker model run hf.co/BAAI/AREX-Base
```

ZiyiXia commited on Aug 11

Commit

d065d3e

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1 Parent(s): 9d57012

Add ModelScope link

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@@ -46,10 +46,10 @@ This repository contains **AREX-Base**, the larger model in the AREX family. It
 **Model Family**
-| Model | Backbone | Context | Positioning |
-| --- | --- | ---: | --- |
-| [AREX-Base](https://huggingface.co/BAAI/AREX-Base) | Qwen3.5-122B-A10B | 256K | Higher-capacity model with the strongest overall AREX results. |
-| [AREX-Turbo](https://huggingface.co/BAAI/AREX-Turbo) | Qwen3.5-4B | 256K | Compact model for lower-cost research and tool-use settings. |
 ## Method

 **Model Family**
+| Model | Backbone | Context | Links | Positioning |
+| --- | --- | ---: | --- | --- |
+| **AREX-Base** | Qwen3.5-122B-A10B | 256K | [🤗 Hugging Face](https://huggingface.co/BAAI/AREX-Base) · [ModelScope](https://modelscope.cn/models/BAAI/AREX-Base) | Higher-capacity model with the strongest overall AREX results. |
+| **AREX-Turbo** | Qwen3.5-4B | 256K | [🤗 Hugging Face](https://huggingface.co/BAAI/AREX-Turbo) · [ModelScope](https://modelscope.cn/models/BAAI/AREX-Turbo) | Compact model for lower-cost research and tool-use settings. |
 ## Method