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--- |
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base_model: google/functiongemma-270m-it |
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library_name: transformers |
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model_name: funcgemma-mobile-actions |
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tags: |
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- generated_from_trainer |
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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 functiongemma-mobile-actions |
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This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). |
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Training was done fully local on a PC with a 32GB Nvidia RTX Pro 4500 GPU (comparable to an RTX 5080) and took roughly 25 mins. |
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The script was derived from the [Google Colab example](https://github.com/google-gemini/gemma-cookbook/tree/main/FunctionGemma) and is available at [ai-bits.org's FunctionGemma repo](https://github.com/ai-bits/functiongemma). |
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For the time being the litertlm model conversion for edge use (Andoid,..) is available in the functiongemma-mobile-actions-litertlm subdirectory here. |
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## Quick start for the converted-to-litertlm model for Android |
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Install the Google AI Edge Gallery app from the Play Store. Start Edge Gallery. |
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In the mobile browser download the .litertlm model version (just one file) from [the subdir here](https://github.com/ai-bits/functiongemma/tree/main/funcgemma-mobile-actions-litertlm). |
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Click the bottom right plus button in the app to install the litertlm model from Downloads. |
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Try it in the now populated Mobile Actions widget. |
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## Quick start in the README.md generated at fine-tuning |
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```python |
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from transformers import pipeline |
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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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## Training procedure |
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This model was trained with SFT. |
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### Framework versions |
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- TRL: 0.25.1 |
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- Transformers: 4.57.1 |
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- Pytorch: 2.9.1 |
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- Datasets: 4.4.1 |
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- Tokenizers: 0.22.1 |
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## Citations |
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Cite TRL as: |
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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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``` |
