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README.md

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+Introducing MermaidSolar_TempVariance_Factual: a 10.7B Parameter Model crafted entirely from synthetic data generated by my own dataset augmentation toolkit. As a passionate enthusiast and researcher in large language models, my journey began with the creation of Mermaid Mistral, a 7B Parameter Model designed to generate knowledge graphs from user input.
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+In my quest to explore the capabilities of dataset augmentation, I honed my toolkit to generate a fresh, synthetic dataset separate from the original organic 500-entry dataset. This new dataset, comprising 17K entries, became the sole training data for MermaidSolar_TempVariance_Factual.
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+Mermaid Mistral, with its 7B parameters, played a pivotal role in this process, as it was responsible for generating the dataset that served as the foundation for the much larger MermaidSolar_TempVariance_Factual.
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+My research is centered on showcasing the potency of dataset augmentation in large language model training. MermaidSolar_TempVariance_Factual serves as a testament to the efficacy of this approach, trained exclusively on synthetic data to demonstrate the power of the augmentation toolkit. I am Troy Andrew Schultz, and this is the culmination of my research endeavor.
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+[Link to Toolkit](https://github.com/Troys-Code/AI_Research/tree/6ef11bd8a3e61539e53ba28b5d420e41b06a154c)
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+This model, MermaidSolar_TempVariance_Factual, with 10.7B parameters, utilizes a dataset created entirely by a 7B Parameter Model, consisting of 17K Entries augmented by Mermaid Mistral outputs.
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+Note: Original ~500 Entry Organic Dataset is now my Entire Eval Dataset :)
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+Training Progress:
+2 Epoch with eval loss as ~0.4 
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+Could probably go a little longer, but this is acceptable for now.
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+Trying to establish some gpu time for training a much bigger model, More->To->Come