Buckets:
| # config for training GPT-2 (774M) | |
| # launch as the following (e.g. in a screen session) and wait ~5 days: | |
| # $ torchrun --standalone --nproc_per_node=8 train.py config/train_gpt774m.py | |
| wandb_log = False | |
| wandb_project = 'complex-rope' | |
| wandb_run_name='gpt2-774M' | |
| base_dir = '' | |
| # these make tokens per batch to be ~0.065M | |
| # 8 batch size * 1024 block size * 1 gradaccum * 8 GPUs = 65,536 | |
| dataset = 'openwebtext' | |
| batch_size = 8 | |
| block_size = 1024 | |
| gradient_accumulation_steps = 1 * 8 | |
| # model | |
| n_layer = 36 | |
| n_head = 20 | |
| n_embd = 1280 | |
| pos_type = 'rope' | |
| base_freq = 10000 | |
| rotate_fraction = 1.0 | |
| thetab_init = 'zero' | |
| dropout = 0.0 | |
| # this makes total number of tokens be 9B (=owt train split size) | |
| max_iters = 100000 | |
| lr_decay_iters = 100000 | |
| warmup_iters = 1000 | |
| # eval stuff | |
| out_dir = 'out-openwebtext' | |
| eval_interval = 1000 | |
| eval_iters = 200 | |
| log_interval = 10 | |
| always_save_checkpoint = False | |
| # weight decay | |
| weight_decay = 1e-2 | |
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