How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)
Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy improvements using less than $10$ parameters rather than the ${\gtrsim 10,000}$ typically demanded by LoRA-based methods. However, that study continued to use gradient-based...
Aditya Kulkarni, Davide Sartor, Carlos J. Soto et al.
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