---
library_name: transformers
license: cc-by-4.0
datasets:
- uonlp/CulturaX
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
# LOLA &mdash; An Open-Source Massively Multilingual Large Language Model


## Model Description

- **Developed by:** DICE Research Group (https://dice-research.org/) @ Paderborn University (https://www.uni-paderborn.de/)
- **Model type:** GPT2 style (decoder-only) with alternating sparse Mixture-of-Experts layers
- **Number of Experts**: 16
- **Model Size**: 1.3 Billion (active*) / 7.4 Billion (total)
- **Language(s) (NLP):** 160+
- **License:** CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
- **Repository:** https://github.com/dice-group/LOLA

<sub>* The number of parameters a model utilizes per token (ref: [Du et al, 2022](https://arxiv.org/abs/2112.06905)). This distinction is crucial for understanding the efficiency and performance of MoE models.</sub>

## How to Get Started with the Model

This pre-trained (causal language modeling) model can only be used for text-generation and requires further fine-tuning on downstream tasks.

### How to use

You can use this model directly with a pipeline for text generation.

```python
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="dice-research/lola_v1", trust_remote_code=True)
>>> generator("The quick brown fox", max_length=13)
[{'generated_text': 'The quick brown fox jumps over the lazy dog.'}]
```

To use the top-k sampling, please set `do_sample` to `True`.

**Note:** The tokenizer used in the model comes from mGPT (https://github.com/ai-forever/mgpt)


## Training Details


### Training Framework

- DeepSpeed Megatron (https://github.com/microsoft/Megatron-DeepSpeed)
- Architecture type: Transformers (Decoder-only) with Mixture-of-Experts (MoE)
- Number of Experts: 16
- Model Size: 1.3 Billion Dense / 7.4 Billion Sparse

### Pretraining Dataset

- CulturaX (https://huggingface.co/datasets/uonlp/CulturaX)
- Total Tokens: 6.3 Trillion
- Total Languages: 167

### LOLA v1 Training:

- Computing cluster: Noctua2 (https://pc2.uni-paderborn.de/hpc-services/available-systems/noctua2)
- Number of GPUs: 96x Nvidia A100 (40GB)
- Training steps: 296000
- Tokens consumed: 465 Billion
- Training time: ~19 days

## Citation
If you use our work in your research, please make sure to cite it:
```bibtex
@misc{srivastava2024lolaopensourcemassively,
      title={LOLA -- An Open-Source Massively Multilingual Large Language Model}, 
      author={Nikit Srivastava and Denis Kuchelev and Tatiana Moteu Ngoli and Kshitij Shetty and Michael Roeder and Diego Moussallem and Hamada Zahera and Axel-Cyrille Ngonga Ngomo},
      year={2024},
      eprint={2409.11272},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.11272}, 
}
```