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fix buggy midtrain and update all kwargs to be idiomatic. that is, argparse uses dashes variables use underscores. the underscores are just a remnant of the previous Configurator object. This is the right way

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Andrej Karpathy
2026-01-13 22:45:27 +00:00
parent 3b50b77ed3
commit 7312ec9898
11 changed files with 144 additions and 139 deletions
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run1000.sh
+5 -5
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@@ -23,15 +23,15 @@ python -m nanochat.dataset -n 16
# start downloading the rest of the shards for a total of 1200 (see below why 1200)
python -m nanochat.dataset -n 1200 &
# todo: download the rest of it
python -m scripts.tok_train --max_chars=4000000000 --vocab_size=65536
python -m scripts.tok_train --max-chars=4000000000 --vocab-size=65536
python -m scripts.tok_eval
# Documenting my process for determining the hyperparameters for this run1000.sh script:
# We want a budget of approx. $1000 ~= 41.6 hours of 8XH100 compute
# 1) I guessed the model size for this to be about depth=32
# 2) Determine the device_batch_size that fits:
# Running the base_train.py script with --depth=32, I saw that --device_batch_size=16
# runs out of memory, but --device_batch_size=8 fits. Inspecting `nvidia-smi` during training,
# Running the base_train.py script with --depth=32, I saw that --device-batch-size=16
# runs out of memory, but --device-batch-size=8 fits. Inspecting `nvidia-smi` during training,
# I saw all GPUs were at about 78/80GB VRAM, so it just barely fits and we have good MFU at ~50%.
# So the training script was running ok and showed:
# Vocab size: 65,536
@@ -73,13 +73,13 @@ python -m scripts.tok_eval
# Number of processes/GPUs to use
NPROC_PER_NODE=8
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=32 --target_param_data_ratio=20 --device_batch_size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=32 --target-param-data-ratio=20 --device-batch-size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_loss
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_eval
# midtrain
# NOTE: ensure that we use the same device_batch_size here as the base training script.
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.mid_train -- --device_batch_size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.mid_train -- --device-batch-size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i mid
# sft