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<a href="https://cyan.kiwi" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-cyankiwi.png" width="800" alt="cyankiwi, the LLM inference agent for open models"></a>
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<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Website</b></td>
<td align="left" style="border:none; padding:4px 0;"><a href="https://cyan.kiwi/" target="_blank">cyan.kiwi</a></td>
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<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Models</b></td>
<td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/cyankiwi" target="_blank">Hugging Face</a> · <a href="https://www.modelscope.ai/organization/cyankiwi" target="_blank">ModelScope</a></td>
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<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Connect</b></td>
<td align="left" style="border:none; padding:4px 0;"><a href="https://x.com/cyankiwi_ai" target="_blank">X</a> · <a href="https://www.reddit.com/r/cyankiwi/" target="_blank">Reddit</a> · <a href="mailto:
[email protected]">Email</a></td>
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<img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-awq.png" width="800" alt="cyankiwi Activation-aware Weight Quantization">
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<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Version</b></td>
<td align="left" style="border:none; padding:4px 0;"><a href="https://github.com/vllm-project/llm-compressor" target="_blank">llm-compressor</a></td>
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<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Calibration</b></td>
<td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset" target="_blank">nvidia/Llama-Nemotron-Post-Training-Dataset</a></td>
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<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Model Size</b></td>
<td align="left" style="border:none; padding:4px 0;">34.12 GB</td>
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<a href="https://cyan.kiwi/workspace?prompt=Deploy%20cyankiwi/bu-30b-a3b-preview-AWQ-8bit" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-button-deploy.png" width="333" alt="Deploy on cyan.kiwi private endpoints"></a>
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BU-30B-A3B-Preview
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<source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/2ccdb752-22fb-41c7-8948-857fc1ad7e24"">
<source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/774a46d5-27a0-490c-b7d0-e65fcbbfa358">
<img alt="Shows a black Browser Use Logo in light color mode and a white one in dark color mode." src="https://github.com/user-attachments/assets/2ccdb752-22fb-41c7-8948-857fc1ad7e24" width="full">
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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