Restructure: nanoGPT at root, custom code in src/

- Move model.py, train.py, configurator.py to root for nanoGPT compatibility
- data/ and config/ directories at root with Shakespeare dataset prep scripts
- src/jlens.py updated to import model from project root
- Cleaned up stale src/config/ and duplicate src/ files
- Fixed .gitignore: exclude out-shakespeare-char/ instead of raw data dirs

1 files changed, 0 insertions, 37 deletions

diff --git a/src/config/train_shakespeare_char.py b/src/config/train_shakespeare_char.py
deleted file mode 100644
index 41c81df..0000000
--- a/src/config/train_shakespeare_char.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# 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