blocks3k/functiongemma-mobile-actions

1---2base_model: google/functiongemma-270m-it3library_name: transformers4model_name: functiongemma-mobile-actions5tags:6- generated_from_trainer7- hf_jobs8- sft9- trl10licence: license11---12 13# Model Card for functiongemma-mobile-actions14 15This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it).16It has been trained using [TRL](https://github.com/huggingface/trl).17 18## Quick start19 20```python21from transformers import pipeline22 23question = "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?"24generator = pipeline("text-generation", model="blocks3k/functiongemma-mobile-actions", device="cuda")25output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]26print(output["generated_text"])27```28 29## Training procedure30 31 32 33 34This model was trained with SFT.35 36### Framework versions37 38- TRL: 0.22.239- Transformers: 4.56.240- Pytorch: 2.9.141- Datasets: 4.4.242- Tokenizers: 0.22.143 44## Citations45 46 47 48Cite TRL as:49    50```bibtex51@misc{vonwerra2022trl,52	title        = {{TRL: Transformer Reinforcement Learning}},53	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},54	year         = 2020,55	journal      = {GitHub repository},56	publisher    = {GitHub},57	howpublished = {\url{https://github.com/huggingface/trl}}58}59```