File size: 1,843 Bytes
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# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.46.0", "datasets"]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
print("Loading dataset...")
dataset = load_dataset("mattPearce/wordpress-blocks-sft", split="train")
dataset_dict = dataset.train_test_split(test_size=41, seed=42)
print("Configuring LoRA for 14B model...")
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)
print("Setting up trainer...")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-Coder-14B-Instruct",
train_dataset=dataset_dict["train"],
eval_dataset=dataset_dict["test"],
peft_config=peft_config,
args=SFTConfig(
output_dir="qwen-wordpress-coder",
num_train_epochs=3,
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
gradient_accumulation_steps=16,
gradient_checkpointing=True,
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.03,
eval_strategy="steps",
eval_steps=25,
logging_steps=5,
save_strategy="steps",
save_steps=50,
save_total_limit=3,
push_to_hub=True,
hub_model_id="mattPearce/qwen-wordpress-coder",
hub_strategy="every_save",
hub_private_repo=False,
bf16=True,
optim="adamw_8bit",
max_grad_norm=1.0,
report_to="trackio",
run_name="qwen-wordpress-14b",
project="wordpress-coder",
),
)
print("Starting training...")
trainer.train()
print("Pushing final model to Hub...")
trainer.push_to_hub()
print("✓ Training complete!")
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