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Is there a good article about efficiently training families of models? If I want to train several models of the same family but with different parameter sizes (to optimize for various production requirements), what is an efficient procedure for training multiple models instead of training them in parallel?

I am going to investigate the impact of batch size and learning rate (LR). I will conduct four different training experiments: Batch size of 1 with a baseline LR Batch size of the entire dataset with the same baseline LR Batch size scaled with a proportionally scaled LR Batch size scaled with LR scaled by the square root of the batch size ratio

Using Callbacks - The fit() method accepts a callbacks argument that lets you specify a list of objects that Keras will call at the start and end of training, at the start and end of each epoch, and even before and after processing each batch. - For example, the ModelCheckpoint callback saves checkpoints of your model at regular intervals during training, by default at the end of each epoch.

Having number of parameters almost equivalent to the number of inputs in your MLP model will almost always lead to overfitting. Overfitting will lead to memorising the inputs rather than learning from them. Another measure of overfitting can be the training loss and validation loss are diverting too much.

Experiment with different network structures and loss functions to optimize performance for your specific tasks. Consider using techniques like weight regularization to prevent overfitting, particularly when dealing with multiple outputs. Monitor the performance of each output carefully to identify and address any imbalances that may occur during training.

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A loss function measures the difference between the predicted output of a model and the actual target value. It quantifies the model's error and guides the optimization process during training. Common loss functions include Mean Squared Error (MSE) for regression tasks and Cross-Entropy Loss for classification tasks. Minimizing the loss function improves the model's accuracy.

Throwing this out there: You can probably have a layer that applies random quantization in the same way drop out is implemented, for example if the drop out was .5 you’d silence the outputs of half the neurons in the layer to help with overfitting, what if instead you quantized at a desired level to those random 50% neurons - that way the model is forced to partially learn to represent the targets with less precision - the downside is it probably would take longer to train to the same level of quality but subsequent quantizations and therefore inference time and cost could be improved with out sacrificing as much quality. Not exactly sure if this would work but maybe - it’s just a thought. Pass it on to someone who’s training llms if you think it’s worth thinking about !

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