File size: 3,797 Bytes
db32e07 | 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 | #!/usr/bin/env python
from __future__ import annotations
import argparse
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))
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from spec_rag.llm_lora import LoRAConfigSpec, build_lora_model
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Fine-tune Llama/Gemma with LoRA on RAG data.")
parser.add_argument("--model-name", required=True)
parser.add_argument("--train-jsonl", required=True)
parser.add_argument("--eval-jsonl", default=None)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--max-length", type=int, default=1024)
parser.add_argument("--batch-size", type=int, default=2)
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--lr", type=float, default=2e-4)
parser.add_argument("--load-in-4bit", action="store_true")
parser.add_argument("--use-chat-template", action="store_true")
return parser.parse_args()
def main() -> None:
args = parse_args()
out_dir = Path(args.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
tokenizer = AutoTokenizer.from_pretrained(args.model_name, use_fast=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
lora_cfg = LoRAConfigSpec()
model = build_lora_model(
args.model_name,
lora_cfg,
load_in_4bit=args.load_in_4bit,
)
data_files = {"train": args.train_jsonl}
if args.eval_jsonl:
data_files["validation"] = args.eval_jsonl
dataset = load_dataset("json", data_files=data_files)
def format_text(example):
if "messages" in example:
if args.use_chat_template and hasattr(tokenizer, "apply_chat_template"):
text = tokenizer.apply_chat_template(example["messages"], tokenize=False)
else:
msgs = example["messages"]
text = "\n".join(f"{m['role']}: {m['content']}" for m in msgs)
else:
prompt = example.get("prompt", "")
target = example.get("target", "")
text = prompt + target
return {"text": text}
dataset = dataset.map(format_text, remove_columns=dataset["train"].column_names)
def tokenize(batch):
return tokenizer(
batch["text"],
truncation=True,
max_length=args.max_length,
padding="max_length",
)
tokenized = dataset.map(tokenize, batched=True)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
train_args = TrainingArguments(
output_dir=str(out_dir),
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
learning_rate=args.lr,
num_train_epochs=args.epochs,
evaluation_strategy="steps" if args.eval_jsonl else "no",
eval_steps=500 if args.eval_jsonl else None,
save_strategy="epoch",
save_total_limit=3, # Keep only last 3 checkpoints
logging_steps=50,
report_to=[],
warmup_steps=100,
fp16=True, # Use mixed precision for faster training
gradient_checkpointing=True, # Save memory
)
trainer = Trainer(
model=model,
args=train_args,
train_dataset=tokenized["train"],
eval_dataset=tokenized.get("validation"),
data_collator=data_collator,
tokenizer=tokenizer,
)
trainer.train()
trainer.save_model(str(out_dir))
if __name__ == "__main__":
main()
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