Model Card for gemma-3-1b-tool-use

This model is a fine-tuned version of google/gemma-3-1b-it for function calling. The code and other resources for this project are linked below. It has been trained using TRL.

Resources:

• YouTube Video
• Blog Post
• GitHub Repo
• Training Data

Quick start

from transformers import pipeline

question = "What day of the week was Nov 4 1998?"
generator = pipeline("text-generation", model="shawhin/gemma-3-1b-tool-use", 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: 0.19.1
• Transformers: 4.53.1
• Pytorch: 2.7.1
• Datasets: 4.0.0
• Tokenizers: 0.21.2

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}}
}