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# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "torch", "transformers"]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
print("🚀 Starting ComfyUI Specialist Training (Production)")
print("=" * 60)
# Load our custom ComfyUI dataset
dataset = load_dataset("lokegud/comfyui-workflows-dataset", split="train")
print(f"📊 Dataset loaded: {len(dataset)} examples")
# Split for evaluation
dataset_split = dataset.train_test_split(test_size=0.15, seed=42)
train_dataset = dataset_split["train"]
eval_dataset = dataset_split["test"]
print(f"📈 Train: {len(train_dataset)} | Eval: {len(eval_dataset)}")
# LoRA configuration - optimized for 1.5B model
peft_config = LoraConfig(
r=32, # Higher rank for better learning
lora_alpha=64,
lora_dropout=0.05,
target_modules=["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
task_type="CAUSAL_LM"
)
# Training configuration - Fixed for 702 examples
# With 702 examples: 597 train, 105 eval
# Steps per epoch: 597 / (2 * 8) = ~37 steps/epoch
# Total steps: 37 * 3 epochs = ~111 steps
training_args = SFTConfig(
output_dir="comfyui-specialist-v1",
num_train_epochs=3,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
gradient_accumulation_steps=8, # Effective batch size: 16
learning_rate=2e-4,
warmup_ratio=0.1, # Warm up for 10% of training (~11 steps)
logging_steps=1, # Log every step
eval_strategy="epoch", # Evaluate after each epoch
save_strategy="epoch", # Save after each epoch
save_total_limit=3,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
push_to_hub=True,
hub_model_id="lokegud/comfyui-specialist-v1",
hub_strategy="end", # Only push final model
hub_private_repo=False,
report_to="trackio",
project="comfyui-specialist",
run_name="production-v1",
gradient_checkpointing=True,
bf16=True, # Faster training with bf16
max_length=2048, # Longer context for full workflows
dataset_text_field="messages", # Chat format
)
print("🔧 Initializing trainer with Qwen2.5-1.5B-Instruct...")
# Initialize trainer
trainer = SFTTrainer(
model="Qwen/Qwen2.5-1.5B-Instruct",
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
args=training_args,
)
print("🏋️ Training ComfyUI Specialist...")
trainer.train()
print("📤 Pushing final model to Hub...")
trainer.push_to_hub()
print("✅ Training complete!")
print(f"📦 Model: lokegud/comfyui-specialist-v1")
print(f"📊 Trackio: https://lokegud-trackio.hf.space/")
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