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1def Prompt_template(query, src_language, trg_language):
2 instruction = f'Translate the following sentences from {src_language} to {trg_language}.'
3 prompt = (
4 'Below is an instruction that describes a task, paired with an input that provides further context. '
5 'Write a response that appropriately completes the request.\n'
6 f'### Instruction:\n{instruction}\n'
7 f'### Input:\n{query}\n### Response:'
8 )
9 return prompt1from transformers import AutoTokenizer, LlamaForCausalLM
2
3model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
4tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
5
6query = "你好，今天是个好日子"
7prompt = Prompt_template(query, 'Chinese', 'English')
8inputs = tokenizer(prompt, return_tensors="pt")
9
10generate_ids = model.generate(inputs.input_ids, max_length=30)
11tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
12# => "Hello, today is a good day"| 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 |
@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.",
}