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Guard against infinite loop when no training shards exist, fix README typo

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Andrej 2026-03-10 22:32:17 -07:00 committed by GitHub
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README.md
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@ -8,7 +8,7 @@ The idea: give an AI agent a small but real LLM training setup and let it experi
## How it works ## How it works
The repo is deliberately kept small and only really has a three files that matter: The repo is deliberately kept small and only really has three files that matter:
- **`prepare.py`** — fixed constants, one-time data prep (downloads training data, trains a BPE tokenizer), and runtime utilities (dataloader, evaluation). Not modified. - **`prepare.py`** — fixed constants, one-time data prep (downloads training data, trains a BPE tokenizer), and runtime utilities (dataloader, evaluation). Not modified.
- **`train.py`** — the single file the agent edits. Contains the full GPT model, optimizer (Muon + AdamW), and training loop. Everything is fair game: architecture, hyperparameters, optimizer, batch size, etc. **This file is edited and iterated on by the agent**. - **`train.py`** — the single file the agent edits. Contains the full GPT model, optimizer (Muon + AdamW), and training loop. Everything is fair game: architecture, hyperparameters, optimizer, batch size, etc. **This file is edited and iterated on by the agent**.

1
prepare.py
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@ -258,6 +258,7 @@ def _document_batches(split, tokenizer_batch_size=128):
val_path = os.path.join(DATA_DIR, VAL_FILENAME) val_path = os.path.join(DATA_DIR, VAL_FILENAME)
if split == "train": if split == "train":
parquet_paths = [p for p in parquet_paths if p != val_path] parquet_paths = [p for p in parquet_paths if p != val_path]
assert len(parquet_paths) > 0, "No training shards found."
else: else:
parquet_paths = [val_path] parquet_paths = [val_path]
epoch = 1 epoch = 1