Devloop4545/functiongemma-270m-it-simple-tool-calling

1---2base_model: google/functiongemma-270m-it3library_name: transformers4model_name: functiongemma-270m-it-simple-tool-calling5tags:6- generated_from_trainer7- trl8- sft9licence: license10---11 12# Model Card for functiongemma-270m-it-simple-tool-calling13 14This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it).15It has been trained using [TRL](https://github.com/huggingface/trl).16 17## Quick start18 19```python20from transformers import pipeline21 22question = "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?"23generator = pipeline("text-generation", model="Devloop4545/functiongemma-270m-it-simple-tool-calling", device="cuda")24output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]25print(output["generated_text"])26```27 28## Training procedure29 30 31 32 33 34This model was trained with SFT.35 36### Framework versions37 38- TRL: 1.12.039- Transformers: 5.16.140- Pytorch: 2.11.0+cu12841- Datasets: 5.0.142- Tokenizers: 0.23.143 44## Citations45 46 47 48Cite TRL as:49    50```bibtex51@software{vonwerra2020trl,52  title   = {{TRL: Transformers Reinforcement Learning}},53  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},54  license = {Apache-2.0},55  url     = {https://github.com/huggingface/trl},56  year    = {2020}57}58```