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---
library_name: transformers
tags:
- multimodal
- gui
license: apache-2.0
datasets:
- chakra-labs/pango
- chakra-labs/pango-sample
language:
- en
base_model:
- ByteDance-Seed/UI-TARS-7B-SFT
pipeline_tag: image-text-to-text
---
# GLADOS-1 — UI-TARS-7B-SFT
### Model Description
GLADOS-1 is the first computer-use (CUA) model post-trained using **collective, crowd-sourced trajectories**.
Leveraging the enourmous [PANGO dataset](https://huggingface.co/datasets/chakra-labs/pango-sample) (with primarily Chrome based interactions), it's purpose is to provide a lense as to what's possible with enormous trajectory sizes in computer use.
It also represents the first open-sourced post-training pipeline for [UI-TARS](https://arxiv.org/pdf/2501.12326), inspired by the existing [Qwen2VL finetuning series](https://github.com/2U1/Qwen2-VL-Finetune).
This model is designed to:
- **Be compliant**. It has been taught to rigorouly follow directions and output action formats compatible with downstream parsers like PyAutoGUI.
- **Understand web productivity applications**. The Pango dataset primarily contains productivity application usage in browser. Consequently in OSWorld results, we observe significantly improved performance on the Chrome task bench.
- **Have strong intuition on visual grounding**. Our experiments are detailed more closely here in our [research blog](TBD).
<div align="left">
<p>
📕 <a href="https://www.chakra.dev/research/glados-1-compute-use-model-crowdsourced-trajectories">Release Blog</a>   |    🤗 <a href="https://github.com/Chakra-Network/GLADOS-1">Code</a>  
|    🔧 <a href="https://github.com/bytedance/UI-TARS/blob/main/README_deploy.md">Deployment (via UI-TARS)</a>    |   
🖥️ <a href="https://github.com/bytedance/UI-TARS-desktop">Running on your own computer (via UI-TARS Desktop)</a>  
</p>
</div>
## Citation
```tex
@misc{chakralabs2025glados-1,
author = {Chakra Labs},
title = {GLADOS-1},
url = {https://github.com/Chakra-Network/GLADOS-1},
year = {2025}
}
``` |