# /// 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 Creative AI Assistant Training (v2 - Expanded)")
print("=" * 60)

# Load expanded multi-domain dataset
dataset = load_dataset("lokegud/creative-ai-knowledge-base", 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 with larger dataset
peft_config = LoraConfig(
    r=32,  # Higher rank for better learning across domains
    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 for 1,177 examples
# With 1,177 examples: 1,000 train, 177 eval
# Steps per epoch: 1000 / (2 * 8) = ~62 steps/epoch
# Total steps: 62 * 3 epochs = ~186 steps
training_args = SFTConfig(
    output_dir="creative-ai-assistant-v2",
    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 (~19 steps)
    logging_steps=5,  # Log every 5 steps
    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/creative-ai-assistant-v2",
    hub_strategy="end",  # Only push final model
    hub_private_repo=False,
    report_to="trackio",
    project="creative-ai-assistant",
    run_name="v2-expanded-1177examples",
    gradient_checkpointing=True,
    bf16=True,  # Faster training with bf16
    max_length=2048,  # Longer context for full content
    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 Creative AI Assistant v2...")
trainer.train()

print("📤 Pushing final model to Hub...")
trainer.push_to_hub()

print("✅ Training complete!")
print(f"📦 Model: lokegud/creative-ai-assistant-v2")
print(f"📊 Trackio: https://lokegud-trackio.hf.space/")
print(f"📚 Dataset: https://huggingface.co/datasets/lokegud/creative-ai-knowledge-base")
print(f"")
print(f"🎯 v2 Capabilities:")
print(f"   - ComfyUI workflows & troubleshooting")
print(f"   - 3D graphics (Blender, USD)")
print(f"   - XR/VR/AR development")
print(f"   - Image generation (SD, SDXL, Flux)")
print(f"   - LLM training & fine-tuning")
print(f"   - Audio synthesis & production")
print(f"   - Anatomy & character design")
print(f"   - Cinematography & camera work")
print(f"   - Scriptwriting & story structure")
print(f"   - Game engine development")
print(f"   - Machine learning fundamentals")