Model Overview
Model Features
Model Capabilities
Use Cases
🚀 LLaMAX: A Multilingual Language Model
LLaMAX is a powerful language model with multilingual capabilities, which doesn't sacrifice its instruction-following abilities. It can support translation between over 100 languages, outperforming similarly scaled LLMs.
🚀 Quick Start
Model Sources
- Paper: LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
- Link: https://arxiv.org/pdf/2407.05975
- Repository: https://github.com/CONE-MT/LLaMAX/
- Demo: https://huggingface.co/spaces/vilarin/LLaMAX3-Translator Thanks for the efforts from @AnnioDance.
Model Description
LLaMAX is a language model that combines powerful multilingual capabilities with excellent instruction-following capabilities. We gathered extensive training sets in 102 languages for the continued pre - training of Llama2 and used the English instruction fine - tuning dataset, Alpaca, to fine - tune its instruction - following abilities.
💻 Usage Examples
Basic Usage
def Prompt_template(query, src_language, trg_language):
instruction = f'Translate the following sentences from {src_language} to {trg_language}.'
prompt = (
'Below is an instruction that describes a task, paired with an input that provides further context. '
'Write a response that appropriately completes the request.\n'
f'### Instruction:\n{instruction}\n'
f'### Input:\n{query}\n### Response:'
)
return prompt
Advanced Usage
from transformers import AutoTokenizer, LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
query = "你好,今天是个好日子"
prompt = Prompt_template(query, 'Chinese', 'English')
inputs = tokenizer(prompt, return_tensors="pt")
generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
# => "Hello, today is a good day"
✨ Features
Effortless Multilingual Translation with a Simple Prompt
LLaMAX supports translation between more than 100 languages, surpassing the performance of similarly scaled LLMs.
Excellent Translation Performance
LLaMAX3 - 8B - Alpaca achieves an average spBLEU score improvement of over 5 points compared to the LLaMA3 - 8B - Alpaca model on the Flores - 101 dataset.
Property | Details |
---|---|
Model Type | LLaMAX is a language model with multilingual and instruction - following capabilities. |
Training Data | Extensive training sets in 102 languages for continued pre - training of Llama2, and the English instruction fine - tuning dataset Alpaca. |
Performance Comparison Tables
System | Size | en - X (COMET) | en - X (BLEU) | zh - X (COMET) | zh - X (BLEU) | de - X (COMET) | de - X (BLEU) | ne - X (COMET) | ne - X (BLEU) | ar - X (COMET) | ar - X (BLEU) | az - X (COMET) | az - X (BLEU) | ceb - X (COMET) | ceb - X (BLEU) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LLaMA3 - 8B - Alpaca | 8B | 67.97 | 17.23 | 64.65 | 10.14 | 64.67 | 13.62 | 62.95 | 7.96 | 63.45 | 11.27 | 60.61 | 6.98 | 55.26 | 8.52 |
LLaMAX3 - 8B - Alpaca | 8B | 75.52 | 22.77 | 73.16 | 14.43 | 73.47 | 18.95 | 75.13 | 15.32 | 72.29 | 16.42 | 72.06 | 12.41 | 68.88 | 15.85 |
System | Size | X - en (COMET) | X - en (BLEU) | X - zh (COMET) | X - zh (BLEU) | X - de (COMET) | X - de (BLEU) | X - ne (COMET) | X - ne (BLEU) | X - ar (COMET) | X - ar (BLEU) | X - az (COMET) | X - az (BLEU) | X - ceb (COMET) | X - ceb (BLEU) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LLaMA3 - 8B - Alpaca | 8B | 77.43 | 26.55 | 73.56 | 13.17 | 71.59 | 16.82 | 46.56 | 3.83 | 66.49 | 10.20 | 58.30 | 4.81 | 52.68 | 4.18 |
LLaMAX3 - 8B - Alpaca | 8B | 81.28 | 31.85 | 78.34 | 16.46 | 76.23 | 20.64 | 65.83 | 14.16 | 75.84 | 15.45 | 70.61 | 9.32 | 63.35 | 12.66 |
📚 Documentation
Supported Languages
Akrikaans (af), Amharic (am), Arabic (ar), Armenian (hy), Assamese (as), Asturian (ast), Azerbaijani (az), Belarusian (be), Bengali (bn), Bosnian (bs), Bulgarian (bg), Burmese (my), Catalan (ca), Cebuano (ceb), Chinese Simpl (zho), Chinese Trad (zho), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Filipino (tl), Finnish (fi), French (fr), Fulah (ff), Galician (gl), Ganda (lg), Georgian (ka), German (de), Greek (el), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Hungarian (hu), Icelandic (is), Igbo (ig), Indonesian (id), Irish (ga), Italian (it), Japanese (ja), Javanese (jv), Kabuverdianu (kea), Kamba (kam), Kannada (kn), Kazakh (kk), Khmer (km), Korean (ko), Kyrgyz (ky), Lao (lo), Latvian (lv), Lingala (ln), Lithuanian (lt), Luo (luo), Luxembourgish (lb), Macedonian (mk), Malay (ms), Malayalam (ml), Maltese (mt), Maori (mi), Marathi (mr), Mongolian (mn), Nepali (ne), Northern Sotho (ns), Norwegian (no), Nyanja (ny), Occitan (oc), Oriya (or), Oromo (om), Pashto (ps), Persian (fa), Polish (pl), Portuguese (pt), Punjabi (pa), Romanian (ro), Russian (ru), Serbian (sr), Shona (sn), Sindhi (sd), Slovak (sk), Slovenian (sl), Somali (so), Sorani Kurdish (ku), Spanish (es), Swahili (sw), Swedish (sv), Tajik (tg), Tamil (ta), Telugu (te), Thai (th), Turkish (tr), Ukrainian (uk), Umbundu (umb), Urdu (ur), Uzbek (uz), Vietnamese (vi), Welsh (cy), Wolof (wo), Xhosa (xh), Yoruba (yo), Zulu (zu)
Model Index
We implement multiple versions of the LLaMAX model, and the model links are as follows:
Model | LLaMAX | LLaMAX - Alpaca |
---|---|---|
Llama - 2 | [Link](https://huggingface.co/LLaMAX/LLaMAX2 - 7B) | [Link](https://huggingface.co/LLaMAX/LLaMAX2 - 7B - Alpaca) |
Llama - 3 | [Link](https://huggingface.co/LLaMAX/LLaMAX3 - 8B - 8B) | [Link](https://huggingface.co/LLaMAX/LLaMAX3 - 8B - 8B - Alpaca) |
📄 License
This project is licensed under the MIT license.
📚 Citation
If our model helps your work, please cite this paper:
@inproceedings{lu-etal-2024-llamax,
title = "{LL}a{MAX}: Scaling Linguistic Horizons of {LLM} by Enhancing Translation Capabilities Beyond 100 Languages",
author = "Lu, Yinquan and
Zhu, Wenhao and
Li, Lei and
Qiao, Yu and
Yuan, Fei",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.631",
doi = "10.18653/v1/2024.findings-emnlp.631",
pages = "10748--10772",
abstract = "Large Language Models (LLMs) demonstrate remarkable translation capabilities in high-resource language tasks, yet their performance in low-resource languages is hindered by insufficient multilingual data during pre-training. To address this, we conduct extensive multilingual continual pre-training on the LLaMA series models, enabling translation support across more than 100 languages. Through a comprehensive analysis of training strategies, such as vocabulary expansion and data augmentation, we develop LLaMAX. Remarkably, without sacrificing its generalization ability, LLaMAX achieves significantly higher translation performance compared to existing open-source LLMs (by more than 10 spBLEU points) and performs on-par with specialized translation model (M2M-100-12B) on the Flores-101 benchmark. Extensive experiments indicate that LLaMAX can serve as a robust multilingual foundation model. The code and the models are publicly available.",
}

