Version 1
: Received: 29 July 2023 / Approved: 31 July 2023 / Online: 1 August 2023 (03:43:37 CEST)
Version 2
: Received: 1 August 2023 / Approved: 2 August 2023 / Online: 2 August 2023 (04:30:51 CEST)
How to cite:
Roumeliotis, K.I.; Tselikas, N.D.; Nasiopoulos, D.K. Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained Model. Preprints2023, 2023072142. https://doi.org/10.20944/preprints202307.2142.v2
Roumeliotis, K.I.; Tselikas, N.D.; Nasiopoulos, D.K. Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained Model. Preprints 2023, 2023072142. https://doi.org/10.20944/preprints202307.2142.v2
Roumeliotis, K.I.; Tselikas, N.D.; Nasiopoulos, D.K. Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained Model. Preprints2023, 2023072142. https://doi.org/10.20944/preprints202307.2142.v2
APA Style
Roumeliotis, K.I., Tselikas, N.D., & Nasiopoulos, D.K. (2023). Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained Model. Preprints. https://doi.org/10.20944/preprints202307.2142.v2
Chicago/Turabian Style
Roumeliotis, K.I., Nikolaos D. Tselikas and Dimitrios K. Nasiopoulos. 2023 "Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained Model" Preprints. https://doi.org/10.20944/preprints202307.2142.v2
Abstract
The rapidly evolving field of artificial intelligence (AI) continues to witness the introduction of innovative open-source pre-trained models, fostering advancements in various applications. One such model is Llama 2, an open-source pre-trained model released by Meta, which has garnered significant attention among early adopters. In addition to exploring the foundational elements of the Llama v2 model, this paper investigates how these early adopters leverage the capabilities of Llama 2 in their AI projects. Through a qualitative study, we delve into the perspectives, experiences, and strategies employed by early adopters to leverage Llama 2's capabilities. For the purpose of data analysis, the capabilities inherent in the Llama 2 model were employed to conduct keyword extraction from the context of the early adopters' case studies. The findings shed light on the model's strengths, weaknesses, and areas of improvement, offering valuable insights for the AI community and Meta to enhance future model iterations. Additionally, we discuss the implications of Llama 2's adoption on the broader open-source AI landscape, addressing challenges and opportunities for developers and researchers in the pursuit of cutting-edge AI solutions. The present study constitutes an early exploration of the Llama 2 pre-trained model, holding promise as a foundational basis for forthcoming research investigations.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
2 August 2023
Commenter:
Konstantinos Roumeliotis
Commenter's Conflict of Interests:
Author
Comment:
After an uninterrupted three-day period of intensive labor, we have meticulously refined the final version of the document, significantly augmenting the quality of the results and the presentation of our research.
Commenter: Konstantinos Roumeliotis
Commenter's Conflict of Interests: Author