Upload train_v2_expanded.py with huggingface_hub

train_v2_expanded.py

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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")