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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 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")
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