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README.md
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After 3 to 4 epochs, the model began to overfit regardless of the strategies employed. Increasing both batch size and the number of epochs resulted in higher final training and evaluation cross-entropy.
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Following an extensive grid search, supervised fine-tuning of [Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) with LoRA+ and the parameters mentioned below yielded the best training and evaluation cross-entropy.
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I've chosen the size ratio between the matrices A and B of 8. The matrix A weights were initialized using the He method, while the matrix B values started with zero. A Gaussian initialization of weights was also considered, but led to a suboptimal result
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the wei
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#### Preprocessing [optional]
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[Coming soon]
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After 3 to 4 epochs, the model began to overfit regardless of the strategies employed. Increasing both batch size and the number of epochs resulted in higher final training and evaluation cross-entropy.
|
90 |
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Following an extensive grid search, supervised fine-tuning of [Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) with LoRA+ and the parameters mentioned below yielded the best training and evaluation cross-entropy.
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I've chosen the size ratio between the matrices A and B of 8. The matrix A weights were initialized using the He method, while the matrix B values started with zero. A Gaussian initialization of weights was also considered, but led to a suboptimal result.
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#### Preprocessing [optional]
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[Coming soon]
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