Tesi etd-11082024-094040 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
MARCUCCETTI, GABRIELE
URN
etd-11082024-094040
Titolo
Adapting Large Language Models through fine tuning techniques for application in social human-robot interaction
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
relatore Galatolo, Federico Andrea
correlatore Cominelli, Lorenzo
correlatore Cominelli, Lorenzo
Parole chiave
- abel
- fine-tuning
- llama
- llm
Data inizio appello
26/11/2024
Consultabilità
Non consultabile
Data di rilascio
26/11/2027
Riassunto
The research area of LLMs, while very recent, is evolving rapidly in many different ways.
In this work, we explore the application of LLMs in the context of humanoid robotics,trough Abel, a humanoid robot. I created a custom dataset of conversation. I selected LLaMA 3, a large language model developed by Meta, with 8B parameters, as the base model for fine-tuning. I adapted LLaMA 3 to understand and respond to the unique interactions expected of a humanoid robot like Abel. This involved training the model on the custom dataset and optimizing key parameters to achieve the best possible performance.
By evaluating the final model against responses generated by external GPT model.
In this work, we explore the application of LLMs in the context of humanoid robotics,trough Abel, a humanoid robot. I created a custom dataset of conversation. I selected LLaMA 3, a large language model developed by Meta, with 8B parameters, as the base model for fine-tuning. I adapted LLaMA 3 to understand and respond to the unique interactions expected of a humanoid robot like Abel. This involved training the model on the custom dataset and optimizing key parameters to achieve the best possible performance.
By evaluating the final model against responses generated by external GPT model.
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