LoRA fine-tuning of Llama3 large model for intelligent fishery field
Abstract With the rapid development of artificial intelligence technology, large language models (LLMs) have shown tremendous potential in multiple fields. This article aims to explore the use of Low Rank Adaptation (LoRA) technology to fine tune the Llama3 model with increased fishery datasets and...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Computing |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s10791-025-09663-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract With the rapid development of artificial intelligence technology, large language models (LLMs) have shown tremendous potential in multiple fields. This article aims to explore the use of Low Rank Adaptation (LoRA) technology to fine tune the Llama3 model with increased fishery datasets and apply it to the field of fisheries to improve the intelligence level of fisheries management. The Llama3 model, developed by Meta, is renowned for its robust language understanding and generation capabilities. However, fine-tuning this model with all parameters is highly resource-intensive and time-consuming. To address these challenges, this study employs LoRA technology, which leverages low-rank decomposition to efficiently fine-tune the Llama3 model. By doing so, we are able to adapt the model to meet the specific requirements of the fishery domain while significantly reducing computational costs and training time. In the field of fisheries, the Llama3 model fine-tuned by LoRA is used to process and analyze textual data related to fisheries, such as water quality and environmental monitoring, fish behavior analysis, fish farming techniques, and interpretation of fisheries policies. By fine-tuning, the model can more accurately understand the professional terminology and contextual information in the field of fisheries, thereby improving the accuracy and efficiency of information processing. This article introduces the basic principles of the Llama3 model and LoRA fine-tuning technology, and elaborates on the specific implementation method of applying LoRA technology to the Llama3 model. A series of experiments were designed to evaluate the application effect of the Llama3 model fine-tuned by LoRA in the field of fisheries. The experimental results indicate that the fine-tuned model demonstrates notable enhancements in performance across various tasks, including fishery text classification, information extraction, and question answering systems. Additionally, the study explores the strengths and challenges of employing LoRA fine-tuning technology within the fishery domain. In terms of challenges, further research is needed on how to optimize the model structure, expand the fishery datasets, improve the model generalization ability, and cope with the complex and changing environment in the fishery field. |
|---|---|
| ISSN: | 2948-2992 |