train_sft_demo.py

# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "datasets", "transformers", "torch", "accelerate"]
# ///

from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import os

# Load a small dataset
dataset = load_dataset("trl-lib/Capybara", split="train[:500]")

# Setup trainer
trainer = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,
    peft_config=LoraConfig(r=16, lora_alpha=32, target_modules="all-linear"),
    args=SFTConfig(
        output_dir="qwen-demo-sft",
        max_steps=100,
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        logging_steps=10,
        push_to_hub=True,
        hub_model_id="passagereptile455/qwen-demo-sft",
        hub_private_repo=True,
    )
)

trainer.train()
trainer.push_to_hub()
print("Training complete! Model pushed to Hub.")