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| #!/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()) |