metadata

tags:
  - Multilingual
license: mit
pipeline_tag: text-generation
base_model: LLaMAX/LLaMAX3-8B-Alpaca

QuantFactory/LLaMAX3-8B-Alpaca-GGUF

This is quantized version of LLaMAX/LLaMAX3-8B-Alpaca created using llama.cpp

Model Description

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/

Model Description

LLaMAX is a language model with powerful multilingual capabilities without loss instruction-following capabilities.

We collected extensive training sets in 102 languages for continued pre-training of Llama2 and leveraged the English instruction fine-tuning dataset, Alpaca, to fine-tune its instruction-following capabilities.

🔥 Effortless Multilingual Translation with a Simple Prompt

LLaMAX supports translation between more than 100 languages, surpassing the performance of similarly scaled LLMs.

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

And then run the following codes to execute translation:

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"

🔥 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.

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

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, the model links are as follows:

Model	LLaMAX	LLaMAX-Alpaca
Llama-2	Link	Link
Llama-3	Link	Link

Model Citation

If our model helps your work, please cite this paper:

@misc{lu2024llamaxscalinglinguistichorizons,
      title={LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages}, 
      author={Yinquan Lu and Wenhao Zhu and Lei Li and Yu Qiao and Fei Yuan},
      year={2024},
      eprint={2407.05975},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.05975}, 
}