Upload train_test.py with huggingface_hub

train_test.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 quick proof-of-concept training...")
+
+# Load tiny subset for quick test
+dataset = load_dataset("trl-lib/Capybara", split="train[:50]")
+
+print(f"📊 Dataset loaded: {len(dataset)} examples")
+
+# LoRA configuration
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
+    task_type="CAUSAL_LM"
+)
+
+# Training configuration
+training_args = SFTConfig(
+    output_dir="comfyui-specialist-test",
+    num_train_epochs=1,
+    max_steps=50,  # Just 50 steps for quick validation
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    logging_steps=5,
+    save_strategy="steps",
+    save_steps=25,
+    push_to_hub=True,
+    hub_model_id="lokegud/comfyui-specialist-test",
+    hub_strategy="every_save",
+    report_to="trackio",
+    project="comfyui-specialist",
+    run_name="quick-test",
+    gradient_checkpointing=True,
+)
+
+print("🔧 Initializing trainer...")
+
+# Initialize trainer
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B",
+    train_dataset=dataset,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+print("🏋️ Training...")
+trainer.train()
+
+print("📤 Pushing to Hub...")
+trainer.push_to_hub()
+
+print("✅ Quick test complete! Model saved to: lokegud/comfyui-specialist-test")