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# /// script
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.36.0",
# "accelerate>=0.24.0",
# "datasets",
# "torch",
# ]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
print("Loading dataset...")
dataset = load_dataset("trl-lib/Capybara", split="train")
dataset = dataset.shuffle(seed=42).select(range(500))
print(f"Using {len(dataset)} examples")
dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
config = SFTConfig(
output_dir="qwen25-test",
push_to_hub=True,
hub_model_id="luiscosio/qwen25-test",
num_train_epochs=1,
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
learning_rate=2e-4,
logging_steps=10,
save_strategy="epoch",
bf16=True,
report_to="none",
)
peft_config = LoraConfig(
r=16,
lora_alpha=32,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "v_proj"],
)
print("Initializing trainer with Qwen2.5-0.5B...")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=dataset_split["train"],
args=config,
peft_config=peft_config,
)
print("Starting training...")
trainer.train()
trainer.push_to_hub()
print("Done!")
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