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Diffstat (limited to 'src/config/train_shakespeare_char.py')
| -rw-r--r-- | src/config/train_shakespeare_char.py | 37 |
1 files changed, 37 insertions, 0 deletions
diff --git a/src/config/train_shakespeare_char.py b/src/config/train_shakespeare_char.py new file mode 100644 index 0000000..41c81df --- /dev/null +++ b/src/config/train_shakespeare_char.py @@ -0,0 +1,37 @@ +# train a miniature character-level shakespeare model +# good for debugging and playing on macbooks and such + +out_dir = 'out-shakespeare-char' +eval_interval = 250 # keep frequent because we'll overfit +eval_iters = 200 +log_interval = 10 # don't print too too often + +# we expect to overfit on this small dataset, so only save when val improves +always_save_checkpoint = False + +wandb_log = False # override via command line if you like +wandb_project = 'shakespeare-char' +wandb_run_name = 'mini-gpt' + +dataset = 'shakespeare_char' +gradient_accumulation_steps = 1 +batch_size = 64 +block_size = 256 # context of up to 256 previous characters + +# baby GPT model :) +n_layer = 6 +n_head = 6 +n_embd = 384 +dropout = 0.2 + +learning_rate = 1e-3 # with baby networks can afford to go a bit higher +max_iters = 5000 +lr_decay_iters = 5000 # make equal to max_iters usually +min_lr = 1e-4 # learning_rate / 10 usually +beta2 = 0.99 # make a bit bigger because number of tokens per iter is small + +warmup_iters = 100 # not super necessary potentially + +# on macbook also add +# device = 'cpu' # run on cpu only +# compile = False # do not torch compile the model |
