metadata
license: gpl-2.0
pipeline_tag: text-generation
Got bored so used nanoGPT to train model on all Python snippets from https://www.kaggle.com/datasets/simiotic/github-code-snippets
Model was trained on default train.py settings, except
eval_intervals=20
eval_iters=40
batch_size=2
gradient_accumulation_steps = 64
This was because I was training it locally on RTX2060 and did not have enough power to train it more. Current model was trained for 8880 iterations. Took around 20 hours. At first I made it only save model after validation loss improved, to not allow overfitting, but after some time I decided to risk it and turned that off and allowed it to save everytime, luckly it worked out fine.