Towards Large Language Model Fine-Tuning

Anika Muller, Santiago Lee, Santiago Haque

Open Access Journal of Engineering Applications · doi:10.55555/oajea.2023.4702.004

1. Introduction

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.

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2. Materials and Methods

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3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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