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)

# 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=40)
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
```

cfli commited on 6 days ago

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@@ -19,9 +19,9 @@ tags:
 <div align="center">
   <img src="assets/arex-logo.png" width="40%" alt="AREX" style="display: block; margin: 0 auto -2px;">
   <div style="margin-bottom: 7px;"><strong>Towards a Recursively Self-Improving Agent for Deep Research</strong></div>
-  <a href="#"><img src="https://img.shields.io/badge/Paper-AREX-B31B1B?style=flat-square&amp;logo=arxiv&amp;logoColor=white" alt="Paper"></a>
-  <a href="https://vectorspacelab.github.io/arex-model/"><img src="https://img.shields.io/badge/Homepage-AREX-24292F?style=flat-square&amp;logo=googlechrome&amp;logoColor=white" alt="Homepage"></a>
-  <a href="https://arex-research.com/"><img src="https://img.shields.io/badge/Live_Demo-AREX-0F766E?style=flat-square&amp;logo=googlechrome&amp;logoColor=white" alt="AREX Demo"></a>
 </div>
 ## Introduction
@@ -151,7 +151,7 @@ AREX is evaluated through a unified long-horizon search-agent interface with `se
 </tr>
 </tbody>
 </table>
-<div style="font-size:12px;color:#6b7280;margin-top:8px;line-height:1.5">* Results reported on the full HLE. Unmarked HLE results use the text-only subset.</div>
 </div>
 ## Inference

 <div align="center">
   <img src="assets/arex-logo.png" width="40%" alt="AREX" style="display: block; margin: 0 auto -2px;">
   <div style="margin-bottom: 7px;"><strong>Towards a Recursively Self-Improving Agent for Deep Research</strong></div>
+  <a href="#"><img src="https://img.shields.io/badge/-Paper-B31B1B?style=for-the-badge&amp;logo=arxiv&amp;logoColor=white" alt="Paper"></a>
+  <a href="https://vectorspacelab.github.io/arex-model/"><img src="https://img.shields.io/badge/-Homepage-24292F?style=for-the-badge&amp;logo=github&amp;logoColor=white" alt="Homepage"></a>
+  <a href="https://arex-research.com/"><img src="https://img.shields.io/badge/-Live_Demo-0F766E?style=for-the-badge&amp;logo=googlechrome&amp;logoColor=white" alt="Live Demo"></a>
 </div>
 ## Introduction
 </tr>
 </tbody>
 </table>
+<div style="font-size:12px;color:#6b7280;margin-top:6px;margin-bottom:-8px;line-height:1.5">* Results reported on the full HLE. Unmarked HLE results use the text-only subset.</div>
 </div>
 ## Inference