---
library_name: transformers
license: apache-2.0
datasets:
- lkevinzc/numia-1.5-qa-concatenated
base_model:
- HuggingFaceTB/FineMath-Llama-3B
---

# Llama-3.2-3B-NuminaQA

## Links

- 📜 [Paper](https://github.com/sail-sg/understand-r1-zero/blob/main/understand-r1-zero.pdf)
- 💻 [GitHub](https://github.com/sail-sg/understand-r1-zero)
- 🤗 [Oat-Zero Collection](https://huggingface.co/collections/sail/oat-zero-understanding-r1-zero-like-training-67dcdb07b9f3eb05f1501c4a)

## Introduction

This model serves as a 3B base in our minimalist R1-Zero recipe. 

Training details: 
- Base model: [HuggingFaceTB/FineMath-Llama-3B](https://huggingface.co/HuggingFaceTB/FineMath-Llama-3B)
- Dataset: [lkevinzc/numia-1.5-qa-concatenated](https://huggingface.co/datasets/lkevinzc/numia-1.5-qa-concatenated)
- Epochs: 2
- Learning rate: 1e-5


## Citation

```latex
@article{liu2025understanding,
  title={Understanding r1-zero-like training: A critical perspective},
  author={Liu, Zichen and Chen, Changyu and Li, Wenjun and Qi, Penghui and Pang, Tianyu and Du, Chao and Lee, Wee Sun and Lin, Min},
  journal={arXiv preprint arXiv:2503.20783},
  year={2025}
}
```