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ebayes/uhura-llama-2-7b-instruct
uhura-llama-2-7b-instruct is a machine learning model from ebayes. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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From the Hugging Face model README
Paper: Uhura: ...
Link: ...
Repository: https://github.com/ebayes/Uhura/
LLaMAX2-7B is a multilingual language base model, developed through continued pre-training on Llama2, and supports over 100 languages. LLaMAX2-7B can serve as a base model to support downstream multilingual tasks but without instruct-following capability.
We further fine-tune LLaMAX2-7B on Alpaca dataset to enhance its instruct-following capabilities. The model is available at https://huggingface.co/LLaMAX/LLaMAX2-7B-Alpaca.
LLaMAX preserves its efficacy in general tasks and improves the performance on multilingual tasks. We fine-tune LLaMAX using only the English training set of downstream task, which also shows significant improvements in non-English. We provide fine-tuning LLaMAX models for the following three tasks:
Math Reasoning: https://huggingface.co/LLaMAX/LLaMAX2-7B-MetaMath
Commonsense Reasoning: https://huggingface.co/LLaMAX/LLaMAX2-7B-X-CSQA
Natural Language Inference: https://huggingface.co/LLaMAX/LLaMAX2-7B-XNLI
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)
If our model helps your work, please cite this paper:
@article{lu2024llamax,
title={LLaMAX: 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},
journal={arXiv preprint arXiv:2407.05975},
year={2024}
}