---
license: apache-2.0
language:
- hmn
datasets:
- allenai/MADLAD-400
- cis-lmu/Glot500
- allenai/c4
library_name: transformers
pipeline_tag: text-generation
tags:
- goldfish
- arxiv:2408.10441
---

# hmn_latn_full

Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the <b>Hmong</b> (Latin script) model trained on 345MB of data (all our data in the language), after accounting for an estimated byte premium of 1.19; content-matched text in Hmong takes on average 1.19x as many UTF-8 bytes to encode as English.
The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs).

Note: hmn_latn is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. None of its contained individual languages are included in Goldfish (for script latn).

All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://www.arxiv.org/abs/2408.10441).

Training code and sample usage: https://github.com/tylerachang/goldfish

Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing)

## Model details:

To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json.
All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
Details for this model specifically:

* Architecture: gpt2
* Parameters: 124770816
* Maximum sequence length: 512 tokens
* Training text data (raw): 411.65MB
* Training text data (byte premium scaled): 345.975MB
* Training tokens: 100051968 (x10 epochs)
* Vocabulary size: 50000
* Compute cost: 5.10651379482624e+17 FLOPs or ~48.3 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):
* 54.54885%: [MADLAD-400 (CommonCrawl)](https://huggingface.co/datasets/allenai/MADLAD-400)
* 45.45115%: [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500), including [MC4](https://huggingface.co/datasets/allenai/c4)


## Citation

If you use this model, please cite:

```
@article{chang-etal-2024-goldfish,
  title={Goldfish: Monolingual Language Models for 350 Languages},
  author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
  journal={Preprint},
  year={2024},
  url={https://www.arxiv.org/abs/2408.10441},
}
```