This is awesome - thanks for sharing. Appreciate the small-scale but comprehensive studies testing out different architectures, model sizes and datasets.
Would be curious to see a version of your model size comparison chart but letting the training continue until perplexity plateaus / begins to overfit. For example: are your larger models performing worse because they are overfitting to a small dataset, or because you are comparing model sizes at a fixed 5 minute computation time - so that the large models just don't get to learn very much in that time.
(Also interesting would be learning curve comparisons between architecture/param count)
Would be curious to see a version of your model size comparison chart but letting the training continue until perplexity plateaus / begins to overfit. For example: are your larger models performing worse because they are overfitting to a small dataset, or because you are comparing model sizes at a fixed 5 minute computation time - so that the large models just don't get to learn very much in that time.
(Also interesting would be learning curve comparisons between architecture/param count)