Text Generation
Transformers
Safetensors
English
qwen3_5_moe
image-text-to-text
agent
deep-research
reasoning
tool-use
long-context
qwen3.5
mixture-of-experts
conversational
Instructions to use BAAI/AREX-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,480 Bytes
c29257f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | #!/usr/bin/env python3
"""Run one AREX BrowseComp generation through an OpenAI-compatible endpoint."""
from __future__ import annotations
import argparse
import os
from prompts import build_messages
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--question", required=True)
parser.add_argument(
"--base-url",
default=os.environ.get("AREX_BASE_URL", "http://127.0.0.1:8000/v1"),
)
parser.add_argument(
"--api-key",
default=os.environ.get("AREX_API_KEY", "EMPTY"),
)
parser.add_argument(
"--model",
default=os.environ.get("AREX_MODEL", "AREX-Base"),
)
parser.add_argument("--max-tokens", type=int, default=8192)
return parser.parse_args()
def main() -> None:
args = parse_args()
try:
from openai import OpenAI
except ImportError as exc:
raise SystemExit("Install the client first: pip install -U openai") from exc
client = OpenAI(
base_url=args.base_url.rstrip("/") + "/",
api_key=args.api_key,
timeout=600.0,
)
response = client.chat.completions.create(
model=args.model,
messages=build_messages(args.question),
max_tokens=args.max_tokens,
temperature=1.0,
top_p=0.95,
presence_penalty=1.5,
extra_body={"top_k": 20},
)
print(response.choices[0].message.content or "")
if __name__ == "__main__":
main()
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