File size: 2,244 Bytes
e3ae57a | 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 | #!/usr/bin/env python3
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
def fine_tune_model():
# Load base model
model_name = "Qwen/Qwen2.5-1.5B-Instruct"
print("Loading base model...")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
device_map="auto",
trust_remote_code=True
)
# Prepare for LoRA
model = prepare_model_for_kbit_training(model)
# LoRA configuration
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Load dataset
print("Loading dataset...")
dataset = load_dataset("chizk/wikipedia-pretrain-zh-tw", split="train[:1000]")
# Tokenize
def tokenize_function(examples):
return tokenizer(
examples["text"],
truncation=True,
max_length=512,
padding="max_length"
)
tokenized_dataset = dataset.map(tokenize_function, batched=True)
# Training arguments
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=1,
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
learning_rate=2e-4,
fp16=True,
logging_steps=10,
save_steps=100,
save_total_limit=1,
report_to="none",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset,
)
# Train
print("Starting training...")
trainer.train()
# Save
print("Saving model...")
model.save_pretrained("./fine_tuned_model")
tokenizer.save_pretrained("./fine_tuned_model")
print("Training complete!")
if __name__ == "__main__":
fine_tune_model()
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