---
base_model: HuggingFaceTB/SmolLM3-3B-Base
library_name: transformers
model_name: smollm3-finetuned-test
tags:
- generated_from_trainer
- sft
- trl
- lora
licence: license
---

# Model Card for smollm3-finetuned-test

This repository provides a LoRA adapter fine-tuned on top of the base model [HuggingFaceTB/SmolLM3-3B-Base](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base).

## Quick start

```python
from transformers import pipeline

generator = pipeline("text-generation", model="Francesco-A/smollm3-finetuned-test", device="cuda")
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?"
output = generator(question, max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

The adapter was trained using [TRL](https://github.com/huggingface/trl).

### Framework versions

- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.6.0+cu124
- Datasets: 4.4.1
- Tokenizers: 0.22.1

## 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}}
}
```