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37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
# train a miniature character-level shakespeare model
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# good for debugging and playing on macbooks and such
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out_dir = 'out-shakespeare-char'
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eval_interval = 250 # keep frequent because we'll overfit
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eval_iters = 200
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log_interval = 10 # don't print too too often
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# we expect to overfit on this small dataset, so only save when val improves
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always_save_checkpoint = False
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wandb_log = False # override via command line if you like
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wandb_project = 'shakespeare-char'
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wandb_run_name = 'mini-gpt'
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dataset = 'shakespeare_char'
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batch_size = 64
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block_size = 256 # context of up to 256 previous characters
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# baby GPT model :)
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n_layer = 6
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n_head = 6
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n_embd = 384
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dropout = 0.2
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learning_rate = 1e-3 # with baby networks can afford to go a bit higher
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max_iters = 5000
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lr_decay_iters = 5000 # make equal to max_iters usually
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min_lr = 1e-4 # learning_rate / 10 usually
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beta2 = 0.99 # make a bit bigger because number of tokens per iter is small
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warmup_iters = 100 # not super necessary potentially
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# on macbook also add
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# device = 'cpu' # run on cpu only
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# compile = False # do not torch compile the model
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