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  [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
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  on one NVidia RTX3090.
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- The training data consisted of **43** conversations (171k tokens / 763 messages)
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  of roughly 4k tokens length. The learning rate is the one that about minimizes the
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  eval loss on one epoch with a constant learning schedule. For the following two epochs
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  what would be normally considered overfitting occurs, but at the same time output
 
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  [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
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  on one NVidia RTX3090.
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+ The training data consisted of **43** conversations (171k tokens / 963 messages)
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  of roughly 4k tokens length. The learning rate is the one that about minimizes the
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  eval loss on one epoch with a constant learning schedule. For the following two epochs
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  what would be normally considered overfitting occurs, but at the same time output