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Verified signatureThis project presents a domain-adapted Large Language Model (LLM) fine-tuned for oceanographic and marine-related natural language understanding tasks using PEFT (Parameter-Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) techniques. The primary goal of this model is to improve conversational understanding and intent recognition for oceanographic, marine science, environmental, and climate-related discussions while maintaining computational efficiency through lightweight adapter-based fine-tuning. The model was developed using the Hugging Face Transformers ecosystem and trained within a cloud-based GPU environment using PyTorch and PEFT libraries. Instead of fully retraining the base model, LoRA adapters were used to efficiently adapt the model to oceanographic intent tasks with significantly reduced memory and compute requirements. This model is capable of assisting with: - Oceanographic intent understanding - Marine-domain conversational AI - Environmental and climate-relate…39b50e0 -
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