TY  - JOUR
AU  - Muller, Anika
AU  - Lee, Santiago
AU  - Haque, Santiago
TI  - Towards Large Language Model Fine-Tuning
JO  - Open Access Journal of Engineering Applications
PY  - 2023
VL  - 47
IS  - 2
SP  - 44
EP  - 57
AB  - Quantifying large language model fine-tuning is a prerequisite for evidence-based policy on tannery wastewater. Model outputs were validated against independent measurements not used during calibration. The model reproduced field observations of large language model fine-tuning with a mean error below 32%. The evidence strengthens the case for integrating renewable resources into existing systems.
KW  - large language model fine-tuning
KW  - principal component analysis
KW  - energy storage
KW  - graphene
KW  - rainfall-runoff modeling
KW  - internet of things
SN  - 3105-7004
DO  - 10.55555/oajea.2023.4702.004
UR  - https://journal.bsaps.com/article/10021
ER  -