<ul><li>Adapting large language models (LLMs) to specialized domains, such as predicting the remaining useful life (RUL) of engines, requires efficient fine-tuning techniques and powerful computational resources.</li><li>The fine-tuning process involves leveraging the Virtuoso-Small-v2 model, a pre-trained 14-billion-parameter language model, and preparing the CMAPSS dataset for the specialized RUL prediction task.</li><li>To ensure computational efficiency, parameter-efficient fine-tuning (PEFT) with LoRA (Low-Rank Adaptation) is employed, updating only a small fraction of Virtuoso's parameters.</li><li>The fine-tuning process is accelerated using an NVIDIA H100 GPU with 80GB of HBM3 memory on Lambda Cloud, demonstrating the efficient use of high-performance computing resources.</li></ul>

Fine-Tuning Virtuoso LLM on Lambda Cloud for Remaining Useful Life Prediction

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