Enhance serve.py to handle additional content types by converting dictionary text and joining list items. Update train.py to replace FastLanguageModel with FastModel and LiteLLMModel, streamline model loading, and adjust dataset preparation logic. Modify config.yaml to change max_samples for testing and add provider information for model configuration.

Add serve.py for model deployment and API integration, update requirements.txt for smolagents with vllm support, and enhance .gitignore to exclude memory snapshot files. Additionally, implement testing configuration in config.yaml and modify train.py for memory tracking and model saving in VLLM format.

Refactor train.py to utilize a comprehensive configuration structure from config.yaml, enhancing model loading, dataset handling, and trainer setup. This update centralizes parameters for model, PEFT, dataset, and training settings, improving maintainability and flexibility.

Add hydra integration and configuration support in train.py, allowing dynamic model loading and training control. Update requirements.txt to include hydra-core dependency and introduce config.yaml for model parameters and training settings.