Upload demo_train.py with huggingface_hub

demo_train.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "datasets>=2.14.0",
+# ]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import os
+
+print("🚀 Starting quick demo training...")
+
+# Load a tiny subset of data
+dataset = load_dataset("trl-lib/Capybara", split="train[:50]")
+print(f"✅ Dataset loaded: {len(dataset)} examples")
+
+# Training configuration
+config = SFTConfig(
+    # CRITICAL: Hub settings
+    output_dir="demo-qwen-sft",
+    push_to_hub=True,
+    hub_model_id="evalstate/demo-qwen-sft",
+    
+    # Quick demo settings
+    max_steps=10,  # Just 10 steps for quick demo
+    per_device_train_batch_size=2,
+    learning_rate=2e-5,
+    
+    # Logging
+    logging_steps=2,
+    save_strategy="no",  # Don't save checkpoints for quick demo
+    
+    # Optimization
+    warmup_steps=2,
+    lr_scheduler_type="constant",
+)
+
+# LoRA configuration (reduces memory)
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "v_proj"],
+)
+
+# Initialize and train
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B",
+    train_dataset=dataset,
+    args=config,
+    peft_config=peft_config,
+)
+
+print("🏃 Training for 10 steps...")
+trainer.train()
+
+print("💾 Pushing to Hub...")
+trainer.push_to_hub()
+
+print("✅ Complete! Model at: https://huggingface.co/evalstate/demo-qwen-sft")