File size: 2,442 Bytes
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# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.36.0",
# "accelerate>=0.24.0",
# "datasets>=2.16.0",
# "trackio",
# ]
# ///
import os
import trackio
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
def main() -> None:
base_model = "Qwen/Qwen2.5-0.5B"
hub_model_id = os.environ.get("HUB_MODEL_ID", "davidsmts/qwen25-0_5b-sft-demo")
project = os.environ.get("TRACKIO_PROJECT", "qwen25_sft_demo")
run_name = os.environ.get("TRACKIO_RUN", "qwen25-0_5b-sft-lora")
print("Loading dataset...")
dataset = load_dataset("trl-lib/Capybara", split="train")
print(f"Loaded {len(dataset)} examples")
print("Creating train/eval split...")
dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
train_ds = dataset_split["train"]
eval_ds = dataset_split["test"]
print(f"Train {len(train_ds)}, Eval {len(eval_ds)}")
trackio.init(
project=project,
run_name=run_name,
config={"model": base_model, "dataset": "trl-lib/Capybara"},
)
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "v_proj"],
)
training_args = SFTConfig(
output_dir="qwen25-0_5b-sft-demo",
push_to_hub=True,
hub_model_id=hub_model_id,
hub_strategy="every_save",
num_train_epochs=1,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-5,
logging_steps=10,
save_strategy="steps",
save_steps=50,
save_total_limit=2,
eval_strategy="steps",
eval_steps=50,
warmup_ratio=0.1,
lr_scheduler_type="cosine",
gradient_checkpointing=True,
fp16=True,
report_to="trackio",
project=project,
run_name=run_name,
)
print("Initializing trainer...")
trainer = SFTTrainer(
model=base_model,
args=training_args,
train_dataset=train_ds,
eval_dataset=eval_ds,
peft_config=peft_config,
)
print("Starting training...")
trainer.train()
print("Pushing to Hub...")
trainer.push_to_hub()
print(f"Complete! Model available at https://huggingface.co/{hub_model_id}")
if __name__ == "__main__":
main()
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