@inproceedings{santos-etal-2024-advancing,
    title = "Advancing Generative {AI} for {P}ortuguese with Open Decoder Gerv{\'a}sio {PT}*",
    author = "Santos, Rodrigo  and
      Silva, Jo{\~a}o Ricardo  and
      Gomes, Lu{\'\i}s  and
      Rodrigues, Jo{\~a}o  and
      Branco, Ant{\'o}nio",
    editor = "Melero, Maite  and
      Sakti, Sakriani  and
      Soria, Claudia",
    booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.sigul-1.3",
    pages = "16--26",
    abstract = "To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gerv{\'a}sio PT*, a strong LLaMA 2 7B model was used as a starting point, and its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose, which are also contributed in this paper. All versions of Gerv{\'a}sio are open source and distributed for free under an open license, including for either research or commercial usage, and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.",
}

To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gerv{\'a}sio PT*, a strong LLaMA 2 7B model was used as a starting point, and its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose, which are also contributed in this paper. All versions of Gerv{\'a}sio are open source and distributed for free under an open license, including for either research or commercial usage, and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.
Santos, Rodrigo Silva, Jo{\~a}o Ricardo Gomes, Lu{\'\i}s Rodrigues, Jo{\~a}o Branco, Ant{\'o}nio
Advancing Generative AI for Portuguese with Open Decoder Gervásio PT*