Fix model recovery and deployment scripts - add safetensors support and Windows compatibility

cloud_deploy.py

@@ -7,6 +7,7 @@ Run this directly on your cloud instance to deploy your trained model
 import os
 import sys
 import logging
+import subprocess
 from pathlib import Path
 
 # Setup logging
@@ -78,18 +79,20 @@ def main():
     
     logger.info(f"Running: {' '.join(cmd)}")
     
-    # Run the command
-    result = os.system(' '.join(cmd))
-    
-    if result == 0:
+    # Run the command using subprocess for better argument handling
+    try:
+        result = subprocess.run(cmd, check=True, capture_output=True, text=True)
         logger.info("✅ Model deployment completed successfully!")
         logger.info(f"🌐 View your model at: https://huggingface.co/{REPO_NAME}")
         logger.info("📊 Quantized models available at:")
         logger.info(f"  - https://huggingface.co/{REPO_NAME}/int8 (GPU optimized)")
         logger.info(f"  - https://huggingface.co/{REPO_NAME}/int4 (CPU optimized)")
         return 0
-    else:
-        logger.error("❌ Model deployment failed!")
+    except subprocess.CalledProcessError as e:
+        logger.error(f"❌ Model deployment failed!")
+        logger.error(f"Error: {e}")
+        logger.error(f"stdout: {e.stdout}")
+        logger.error(f"stderr: {e.stderr}")
         return 1
 
 if __name__ == "__main__":

quick_deploy.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""
+Quick Model Deployment Script
+Direct deployment without argument parsing issues
+"""
+
+import os
+import sys
+import logging
+from pathlib import Path
+
+# Add src to path for imports
+sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
+
+# Setup logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def main():
+    """Direct deployment without argument parsing"""
+    
+    # Configuration
+    MODEL_PATH = "/output-checkpoint"
+    REPO_NAME = "Tonic/smollm3-finetuned"
+    HF_TOKEN = os.getenv('HF_TOKEN')
+    
+    if not HF_TOKEN:
+        logger.error("❌ HF_TOKEN not set")
+        return 1
+    
+    if not Path(MODEL_PATH).exists():
+        logger.error(f"❌ Model path not found: {MODEL_PATH}")
+        return 1
+    
+    logger.info("✅ Model files validated")
+    
+    # Import and run the recovery pipeline directly
+    try:
+        from recover_model import ModelRecoveryPipeline
+        
+        # Initialize pipeline
+        pipeline = ModelRecoveryPipeline(
+            model_path=MODEL_PATH,
+            repo_name=REPO_NAME,
+            hf_token=HF_TOKEN,
+            private=False,
+            quantize=True,
+            quant_types=["int8_weight_only", "int4_weight_only"],
+            author_name="Tonic",
+            model_description="A fine-tuned SmolLM3 model for improved text generation and conversation capabilities"
+        )
+        
+        # Run the complete pipeline
+        success = pipeline.run_complete_pipeline()
+        
+        if success:
+            logger.info("✅ Model deployment completed successfully!")
+            logger.info(f"🌐 View your model at: https://huggingface.co/{REPO_NAME}")
+            return 0
+        else:
+            logger.error("❌ Model deployment failed!")
+            return 1
+            
+    except Exception as e:
+        logger.error(f"❌ Error during deployment: {e}")
+        return 1
+
+if __name__ == "__main__":
+    exit(main())