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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 | """Prepare MLX-LM-compatible train/valid files from existing SFT data."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Dict, List
from transformers import AutoTokenizer
ROOT = Path(__file__).resolve().parents[1]
TRUNCATION_MARKER = "\n...[truncated observation]...\n"
def load_jsonl(path: Path) -> List[Dict[str, object]]:
with path.open("r", encoding="utf-8") as handle:
return [json.loads(line) for line in handle if line.strip()]
def dump_jsonl(path: Path, rows: List[Dict[str, object]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, sort_keys=True) + "\n")
def trim_prompt_to_budget(prompt: str, tokenizer, budget: int) -> str:
prompt_ids = tokenizer.encode(prompt, add_special_tokens=False)
if len(prompt_ids) <= budget:
return prompt
marker_ids = tokenizer.encode(TRUNCATION_MARKER, add_special_tokens=False)
marker_len = len(marker_ids)
if budget <= marker_len + 8:
return tokenizer.decode(prompt_ids[-budget:])
remaining = budget - marker_len
head_len = max(1, int(remaining * 0.55))
tail_len = max(1, remaining - head_len)
trimmed_ids = prompt_ids[:head_len] + marker_ids + prompt_ids[-tail_len:]
if len(trimmed_ids) > budget:
trimmed_ids = trimmed_ids[:budget]
return tokenizer.decode(trimmed_ids, skip_special_tokens=False)
def rendered_length(prompt: str, completion: str, tokenizer) -> int:
messages = [
{"role": "user", "content": prompt},
{"role": "assistant", "content": completion},
]
return len(tokenizer.apply_chat_template(messages, return_dict=False))
def normalize_record(record: Dict[str, object], tokenizer, max_seq_length: int) -> tuple[Dict[str, object] | None, Dict[str, int]]:
prompt = str(record["prompt"])
completion = str(record["completion"])
stats = {"trimmed": 0, "dropped": 0}
completion_ids = tokenizer.encode(completion, add_special_tokens=False)
prompt_budget = max_seq_length - len(completion_ids) - 32
if prompt_budget <= 0:
stats["dropped"] = 1
return None, stats
normalized_prompt = trim_prompt_to_budget(prompt, tokenizer, prompt_budget)
while rendered_length(normalized_prompt, completion, tokenizer) > max_seq_length and prompt_budget > 64:
prompt_budget = max(64, int(prompt_budget * 0.9))
normalized_prompt = trim_prompt_to_budget(prompt, tokenizer, prompt_budget)
if rendered_length(normalized_prompt, completion, tokenizer) > max_seq_length:
stats["dropped"] = 1
return None, stats
if normalized_prompt != prompt:
stats["trimmed"] = 1
text = f"{normalized_prompt}\n{completion}"
normalized = dict(record)
normalized["prompt"] = normalized_prompt
normalized["text"] = text
return normalized, stats
def transform_split(src: Path, dst: Path, tokenizer, max_seq_length: int) -> Dict[str, int]:
rows = load_jsonl(src)
normalized_rows: List[Dict[str, object]] = []
stats = {"input_examples": len(rows), "written_examples": 0, "trimmed_examples": 0, "dropped_examples": 0}
for row in rows:
normalized, row_stats = normalize_record(row, tokenizer, max_seq_length)
stats["trimmed_examples"] += row_stats["trimmed"]
stats["dropped_examples"] += row_stats["dropped"]
if normalized is not None:
normalized_rows.append(normalized)
stats["written_examples"] = len(normalized_rows)
dump_jsonl(dst, normalized_rows)
return stats
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--source-root", default="artifacts/lora_qwen3_4b/data")
parser.add_argument("--output-root", default="artifacts/mlx_qwen3_4b/data")
parser.add_argument("--model", default="Qwen/Qwen3.5-4B")
parser.add_argument("--max-seq-length", type=int, default=1024)
parser.add_argument("--include-valid", action="store_true")
parser.add_argument("--force", action="store_true")
args = parser.parse_args()
source_root = (ROOT / args.source_root).resolve()
output_root = (ROOT / args.output_root).resolve()
output_root.mkdir(parents=True, exist_ok=True)
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
mapping = {source_root / "train.jsonl": output_root / "train.jsonl"}
if args.include_valid:
mapping[source_root / "eval.jsonl"] = output_root / "valid.jsonl"
summary: Dict[str, object] = {
"model": args.model,
"max_seq_length": args.max_seq_length,
"splits": {},
}
for src, dst in mapping.items():
if not src.exists():
raise FileNotFoundError(f"Missing source file: {src}")
if dst.exists() and not args.force:
continue
summary["splits"][dst.stem] = transform_split(src, dst, tokenizer, args.max_seq_length)
valid_path = output_root / "valid.jsonl"
if not args.include_valid and valid_path.exists():
valid_path.unlink()
summary_path = output_root.parent / "prepare_stats.json"
summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8")
print(output_root)
print(json.dumps(summary, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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