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  ---
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- base_model: Qwen/Qwen2.5-7B-Instruct
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- library_name: transformers
 
 
 
 
 
 
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  tags:
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- - generated_from_trainer
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- - alignment-handbook
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- - sft
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- - trl
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- licence: license
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  ---
 
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- # Model Card for None
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- This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
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- It has been trained using [TRL](https://github.com/huggingface/trl).
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- ## Quick start
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-
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- ```python
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- from transformers import pipeline
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-
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- question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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- generator = pipeline("text-generation", model="None", device="cuda")
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- output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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- print(output["generated_text"])
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- ```
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-
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- ## Training procedure
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-
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ruichen-zheng-ucla/huggingface/runs/lsi98ycc)
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-
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-
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- This model was trained with SFT.
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-
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- ### Framework versions
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-
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- - TRL: 0.23.0
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- - Transformers: 4.56.2
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- - Pytorch: 2.6.0+cu126
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- - Datasets: 4.1.1
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- - Tokenizers: 0.22.1
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-
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- ## Citations
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-
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-
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-
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- Cite TRL as:
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-
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- ```bibtex
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- @misc{vonwerra2022trl,
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- title = {{TRL: Transformer Reinforcement Learning}},
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- author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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- year = 2020,
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- journal = {GitHub repository},
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- publisher = {GitHub},
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- howpublished = {\url{https://github.com/huggingface/trl}}
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- }
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- ```
 
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+ license: mit
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+ datasets:
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+ - UI-Simulator/UI-Simulator_android_data
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+ language:
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+ - en
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+ base_model:
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+ - Qwen/Qwen2.5-7B-Instruct
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+ pipeline_tag: text-generation
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  tags:
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+ - agent
 
 
 
 
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  ---
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+ # Model Card for Model ID
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+ This is the UI-Simulator model that is trained on RAG-free Android UI-Simulator. It is trained to solve AndroidWorld tasks.
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+ ## Uses
 
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+ We provide a script for loading the model and run AndroidWorld evaluation [here](https://github.com/WadeYin9712/UI-Simulator/blob/main/android_world/run.py)