Upload train_demo.py with huggingface_hub

train_demo.py

@@ -16,57 +16,60 @@ from trl import SFTTrainer, SFTConfig
 # Initialize Trackio for real-time monitoring
 trackio.init(
     project="qwen-demo-sft",
-    space_id="evalstate/training-demo-dashboard",
+    space_id="evalstate/trackio-demo",  # Will auto-create if doesn't exist
     config={
         "model": "Qwen/Qwen2.5-0.5B",
         "dataset": "trl-lib/Capybara",
+        "dataset_size": 50,
         "learning_rate": 2e-5,
         "max_steps": 20,
-        "peft_method": "LoRA",
+        "demo": True,
     }
 )
 
-# Load small subset for quick demo
+# Load dataset (only 50 examples for quick demo)
 dataset = load_dataset("trl-lib/Capybara", split="train[:50]")
 print(f"✅ Dataset loaded: {len(dataset)} examples")
+print(f"📝 Sample: {dataset[0]}")
 
 # Training configuration
 config = SFTConfig(
-    # CRITICAL: Hub settings
+    # Hub settings - CRITICAL for saving results
     output_dir="qwen-demo-sft",
     push_to_hub=True,
     hub_model_id="evalstate/qwen-demo-sft",
+    hub_strategy="end",  # Push only at end for demo
     
-    # Quick demo settings
-    max_steps=20,
+    # Training parameters (minimal for quick demo)
+    max_steps=20,  # Very short training
     per_device_train_batch_size=2,
     gradient_accumulation_steps=2,
     learning_rate=2e-5,
     
     # Logging
     logging_steps=5,
-    save_strategy="steps",
-    save_steps=20,
+    save_strategy="no",  # Don't save checkpoints during training
     
     # Optimization
-    warmup_steps=2,
+    warmup_steps=5,
     lr_scheduler_type="cosine",
     
     # Monitoring
     report_to="trackio",
 )
 
-# LoRA configuration
+# LoRA configuration (reduces memory usage)
 peft_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
+    r=8,  # Small rank for demo
+    lora_alpha=16,
     lora_dropout=0.05,
     bias="none",
     task_type="CAUSAL_LM",
     target_modules=["q_proj", "v_proj"],
 )
 
-# Initialize and train
+# Initialize trainer
+print("🚀 Initializing trainer...")
 trainer = SFTTrainer(
     model="Qwen/Qwen2.5-0.5B",
     train_dataset=dataset,
@@ -74,14 +77,17 @@ trainer = SFTTrainer(
     peft_config=peft_config,
 )
 
-print("🚀 Starting training...")
+# Train
+print("🔥 Starting training (20 steps)...")
 trainer.train()
 
-print("💾 Pushing to Hub...")
+# Push to Hub
+print("💾 Pushing model to Hub...")
 trainer.push_to_hub()
 
 # Finish Trackio tracking
 trackio.finish()
 
-print("✅ Complete! Model at: https://huggingface.co/evalstate/qwen-demo-sft")
-print("📊 View metrics at: https://huggingface.co/spaces/evalstate/training-demo-dashboard")
+print("✅ Training complete!")
+print(f"📦 Model: https://huggingface.co/evalstate/qwen-demo-sft")
+print(f"📊 Metrics: https://huggingface.co/spaces/evalstate/trackio-demo")