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library_name: peft
model_name: SmolLMathematician-3B
tags:
- base_model:adapter:HuggingFaceTB/SmolLM3-3B-Base
- lora
- sft
- transformers
- trl
licence: license
base_model: HuggingFaceTB/SmolLM3-3B-Base
pipeline_tag: text-generation
datasets:
- TIGER-Lab/MathInstruct
---
# Model Card for SmolLMathematician-3B
This model is a fine-tuned version of [HuggingFaceTB/SmolLM3-3B-Base](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl) on [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct).
Training Trackio:
## Quick start
```python
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="None", 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
- PEFT 0.17.1
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.1.1
- 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}}
}
``` |