Instructions to use browser-use/bu-30b-a3b-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use browser-use/bu-30b-a3b-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="browser-use/bu-30b-a3b-preview") 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("browser-use/bu-30b-a3b-preview") model = AutoModelForImageTextToText.from_pretrained("browser-use/bu-30b-a3b-preview") 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 browser-use/bu-30b-a3b-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "browser-use/bu-30b-a3b-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "browser-use/bu-30b-a3b-preview", "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/browser-use/bu-30b-a3b-preview
- SGLang
How to use browser-use/bu-30b-a3b-preview 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 "browser-use/bu-30b-a3b-preview" \ --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": "browser-use/bu-30b-a3b-preview", "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 "browser-use/bu-30b-a3b-preview" \ --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": "browser-use/bu-30b-a3b-preview", "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 browser-use/bu-30b-a3b-preview with Docker Model Runner:
docker model run hf.co/browser-use/bu-30b-a3b-preview
BU-30B-A3B-Preview
Meet BU-30B-A3B-Preview — bringing SoTA Browser Use capabilities in a small model that can be hosted on a single GPU.
This model is heavily trained to be used with browser-use OSS library and provides comprehensive browsing capabilities with superior DOM understanding and visual reasoning.
Quickstart (BU Cloud)
You can directly use this model at BU Cloud. Simply
- Get your API key from BU Cloud
- Set environment variable: export BROWSER_USE_API_KEY="your-key"
- Install the browser-use library following the instructions here and run
from dotenv import load_dotenv
from browser_use import Agent, ChatBrowserUse
load_dotenv()
llm = ChatBrowserUse(
model='browser-use/bu-30b-a3b-preview', # BU Open Source Model!!
)
agent = Agent(
task='Find the number of stars of browser-use and stagehand. Tell me which one has more stars :)',
llm=llm,
flash_mode=True
)
agent.run_sync()
Quickstart (vLLM)
We recommend using this model with vLLM.
Installation
Make sure to install vllm >= 0.12.0:
pip install vllm --upgrade
Serve
A simple launch command is:
vllm serve browser-use/bu-30b-a3b-preview \
--max-model-len 65536 \
--host 0.0.0.0 \
--port 8000
which will create an OpenAI compatible endpoint at localhost that you can use with.
from dotenv import load_dotenv
from browser_use import Agent, ChatOpenAI
load_dotenv()
llm = ChatOpenAI(
base_url='http://localhost:8000/v1',
model='browser-use/bu-30b-a3b-preview',
temperature=0.6,
top_p=0.95,
dont_force_structured_output=True, # speed up by disabling structured output
)
agent = Agent(
task='Find the number of stars of browser-use and stagehand. Tell me which one has more stars :)',
llm=llm,
)
agent.run_sync()
Model Details
| Property | Value |
|---|---|
| Base Model | Qwen/Qwen3-VL-30B-A3B-Instruct |
| Parameters | 30B total, 3B active (MoE) |
| Context Length | 65,536 tokens |
| Architecture | Vision-Language Model (Mixture of Experts) |
Links
- 🌐 Browser Use Cloud
- 📚 Documentation
- 💻 GitHub
- 💬 Discord
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