mumble-cleanup-training / scripts /prepare_mlx_data.py
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Initial upload: 50k synthetic corpus + 14 training scripts + configs
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"""
Prepare training data for MLX-LM.
MLX-LM expects a directory containing:
train.jsonl - chat-format training data
valid.jsonl - chat-format validation data (optional)
test.jsonl - chat-format test data (optional)
Each line is a JSON object with one of:
{"messages": [...]} (chat format, our existing format)
{"prompt": ..., "completion": ...}
{"text": "..."}
This script splits our existing corpus into train/valid splits and writes
them into the MLX-LM expected directory layout.
Usage:
python scripts/prepare_mlx_data.py \
--input data/synthetic/corpus_50k.jsonl \
--output data/mlx_dataset \
--valid-count 100 \
--seed 42
"""
import argparse
import json
import random
from pathlib import Path
def main():
parser = argparse.ArgumentParser(description="Prepare MLX-LM training data")
parser.add_argument("--input", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--valid-count", type=int, default=100)
parser.add_argument("--max-samples", type=int, default=None)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
random.seed(args.seed)
rows = []
with args.input.open("r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
rows.append(line)
if args.max_samples:
rows = rows[: args.max_samples]
random.shuffle(rows)
valid_rows = rows[: args.valid_count]
train_rows = rows[args.valid_count :]
args.output.mkdir(parents=True, exist_ok=True)
train_path = args.output / "train.jsonl"
valid_path = args.output / "valid.jsonl"
with train_path.open("w", encoding="utf-8") as f:
f.write("\n".join(train_rows) + "\n")
with valid_path.open("w", encoding="utf-8") as f:
f.write("\n".join(valid_rows) + "\n")
print(f"Wrote {len(train_rows)} train rows to {train_path}")
print(f"Wrote {len(valid_rows)} valid rows to {valid_path}")
if __name__ == "__main__":
main()