1.5 KiB
1.5 KiB
base_model, library_name, model_name, tags, licence
| base_model | library_name | model_name | tags | licence | |||
|---|---|---|---|---|---|---|---|
| google/functiongemma-270m-it | transformers | functiongemma-270m-it-simple-tool-calling |
|
license |
Model Card for functiongemma-270m-it-simple-tool-calling
This model is a fine-tuned version of google/functiongemma-270m-it. It has been trained using TRL.
Quick start
from transformers import pipeline
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?"
generator = pipeline("text-generation", model="tommal/functiongemma-270m-it-simple-tool-calling", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 0.26.2
- Transformers: 4.57.3
- Pytorch: 2.9.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.1
Citations
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
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},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}