README.md

---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: gemma-3-1b-tool-use
tags:
- generated_from_trainer
- trl
- sft
licence: license
datasets:
- shawhin/tool-use-finetuning
---

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

This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/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](https://github.com/huggingface/trl).

Resources:
- [YouTube Video](https://youtu.be/fAFJYbtTsC0)
- [Blog Post](https://medium.com/@shawhin/fine-tuning-llms-for-tool-use-5f1db03d7c55)
- [GitHub Repo](https://github.com/ShawhinT/llm-tool-use-ft)
- [Training Data](https://huggingface.co/datasets/shawhin/tool-use-finetuning)

## Quick start

```python
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:
    
```bibtex
@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}}
}
```