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d63a1ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | """Run MLX LoRA training as the default local Mac training path."""
from __future__ import annotations
import argparse
import json
import shlex
import subprocess
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
def write_json(path: Path, payload: dict) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="Qwen/Qwen3.5-4B")
parser.add_argument("--source-root", default="artifacts/lora_qwen3_4b/data")
parser.add_argument("--output-root", default="artifacts/mlx_qwen3_4b")
parser.add_argument("--iters", type=int, default=120)
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument("--grad-accumulation-steps", type=int, default=8)
parser.add_argument("--learning-rate", type=float, default=5e-5)
parser.add_argument("--num-layers", type=int, default=8)
parser.add_argument("--max-seq-length", type=int, default=1024)
parser.add_argument("--steps-per-report", type=int, default=1)
parser.add_argument("--save-every", type=int, default=20)
parser.add_argument("--seed", type=int, default=7)
parser.add_argument("--fresh-start", action="store_true")
parser.add_argument("--include-valid", action="store_true")
args = parser.parse_args()
output_root = (ROOT / args.output_root).resolve()
data_root = output_root / "data"
log_path = output_root / "logs" / "mlx_train.log"
manifest_path = output_root / "run_manifest.json"
adapter_root = output_root / "adapters"
adapter_file = adapter_root / "adapters.safetensors"
speed_path = output_root / "metrics" / "speed_mlx.json"
output_root.mkdir(parents=True, exist_ok=True)
if args.fresh_start:
for rel in [log_path, speed_path, output_root / "training_summary.json", adapter_file]:
if rel.exists():
rel.unlink()
prepare_cmd = [
sys.executable,
"scripts/prepare_mlx_data.py",
"--source-root",
args.source_root,
"--output-root",
str(data_root.relative_to(ROOT)),
"--model",
args.model,
"--max-seq-length",
str(args.max_seq_length),
"--force",
]
if args.include_valid:
prepare_cmd.append("--include-valid")
subprocess.run(prepare_cmd, cwd=ROOT, check=True)
cmd = [
sys.executable,
"-m",
"mlx_lm",
"lora",
"--model",
args.model,
"--train",
"--data",
str(data_root),
"--mask-prompt",
"--num-layers",
str(args.num_layers),
"--batch-size",
str(args.batch_size),
"--iters",
str(args.iters),
"--learning-rate",
str(args.learning_rate),
"--steps-per-report",
str(args.steps_per_report),
"--steps-per-eval",
"1000000",
"--save-every",
str(args.save_every),
"--grad-accumulation-steps",
str(args.grad_accumulation_steps),
"--grad-checkpoint",
"--adapter-path",
str(adapter_root),
"--max-seq-length",
str(args.max_seq_length),
"--seed",
str(args.seed),
]
if not args.fresh_start and adapter_file.exists():
cmd.extend(["--resume-adapter-file", str(adapter_file)])
write_json(
manifest_path,
{
"status": "starting_training",
"trainer": "mlx_lm_lora",
"model": args.model,
"data_root": str(data_root),
"output_root": str(output_root),
"command": cmd,
"fresh_start": args.fresh_start,
},
)
log_path.parent.mkdir(parents=True, exist_ok=True)
with log_path.open("a", encoding="utf-8") as handle:
handle.write("\n===== mlx_lm_lora =====\n")
handle.write("COMMAND: " + " ".join(shlex.quote(part) for part in cmd) + "\n")
handle.flush()
process = subprocess.run(cmd, cwd=ROOT, stdout=handle, stderr=subprocess.STDOUT, text=True)
subprocess.run([sys.executable, "scripts/save_mlx_speed.py", "--log-path", str(log_path), "--output-path", str(speed_path)], cwd=ROOT, check=False)
summary = {
"status": "finished" if process.returncode == 0 else "failed",
"trainer": "mlx_lm_lora",
"return_code": process.returncode,
"log_path": str(log_path),
"speed_path": str(speed_path),
"adapter_root": str(adapter_root),
}
write_json(output_root / "training_summary.json", summary)
write_json(manifest_path, summary)
if process.returncode != 0:
raise SystemExit(process.returncode)
if __name__ == "__main__":
main()
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