This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a single GPU is possible. In addition, we release a collection of datasets for conversational AI in Italian. The datasets on which we fine-tuned Fauno include various topics such as general question answering, computer science, and medical questions. We release our code and datasets on https://github.com/RSTLess-research/Fauno-Italian-LLM

Fauno: The Italian Large Language Model that will leave you senza parole! / Bacciu, Andrea; Trappolini, Giovanni; Santilli, Andrea; Rodolà, Emanuele; Silvestri, Fabrizio. - 3448:(2023), pp. 9-17. (Intervento presentato al convegno IIR2023: 13th Italian Information Retrieval Workshop tenutosi a Pisa; Italy).

Fauno: The Italian Large Language Model that will leave you senza parole!

Andrea Bacciu
Primo
;
Giovanni Trappolini
Secondo
;
Emanuele Rodolà
Penultimo
;
Fabrizio Silvestri
Ultimo
2023

Abstract

This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a single GPU is possible. In addition, we release a collection of datasets for conversational AI in Italian. The datasets on which we fine-tuned Fauno include various topics such as general question answering, computer science, and medical questions. We release our code and datasets on https://github.com/RSTLess-research/Fauno-Italian-LLM
2023
IIR2023: 13th Italian Information Retrieval Workshop
large language model; large, language; model; llm
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Fauno: The Italian Large Language Model that will leave you senza parole! / Bacciu, Andrea; Trappolini, Giovanni; Santilli, Andrea; Rodolà, Emanuele; Silvestri, Fabrizio. - 3448:(2023), pp. 9-17. (Intervento presentato al convegno IIR2023: 13th Italian Information Retrieval Workshop tenutosi a Pisa; Italy).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1698182
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