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pipeline_tag: text-generation
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# Model Card for
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## Model Details
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<!-- Provide a longer summary of what this model is. -->
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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pipeline_tag: text-generation
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# Model Card for SurfMine
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## Model Details
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This is a model created to generate human readable surf forecast text based on live user inputs from weather sources.
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### Model Description and Introduction
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There are many surf forecasting models available, including the WAVEWATCH model from NOAA, the LOUTS model from Surfline and numerous other models on the internet that claim to deliver the most accurate and up-to-date information. The most prominent comes from a company called Surfline. They have increasingly pushed more of their features into a pay-to-play model. They have also acquired a few of their competitors. As a result, they have become the only reliable source for surf forecasts for certain known spots. To gain a deeper understanding of how to develop a deep learning model for a subject I am both interested in and passionate about, this is my attempt build a surf-forecast model that takes historical NOAA Buoy Data, and outputs a surf forecast for that input text, in the flavor of a seasoned surf forecaster/reporter. Current models struggle to capture the essence (saltiness/coolness factor) of these veteran surf reporters, I aim bridge this gap through specific prompt tuning and LoRA fine-tuning.
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