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_test.py for testing chat completion functionality with the OpenAI client. Update serve.py to use FastModel for improved performance and adjust input handling for optional image processing. Include debugging output for better error tracking.

Update requirements.txt to include sse-starlette dependency, enhance serve.py with additional imports for FastLanguageModel and FastVisionModel, and refactor train.py for improved organization and memory tracking during model training.

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 SFTTrainer configuration in train.py to remove data_collator from the SFT config, preventing duplication and enhancing clarity in trainer setup.

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.

Refactor model loading in train.py to use a default model name parameter, enhancing flexibility. Adjust configuration for max sequence length and dtype for improved clarity and consistency.

Refactor imports in train.py to improve organization and clarity, adding DataCollatorForLanguageModeling for enhanced data handling during training.

Add DataCollatorForLanguageModeling to trainer configuration in train.py for improved data handling during training.

Update requirements.txt to specify unsloth version 2025.4.5 and refactor imports in train.py for improved organization and clarity.

Remove unused dataset_text_field parameter from create_trainer function in train.py to streamline trainer configuration.

Update trainer configuration in train.py to align evaluation strategy with save strategy. Set eval_steps to match save_steps for consistent evaluation frequency.

Refactor trainer configuration in train.py for improved clarity. Clean up comments and ensure consistent formatting in evaluation strategy and model selection parameters.

Refactor train.py to improve code readability and organization. Adjust logging setup for clarity, streamline dependency installation commands, and enhance dataset splitting and formatting processes. Ensure consistent formatting in log messages and code structure.

Enhance training script for SmolLM2-135M model by adding logging functionality, improving error handling, and implementing dataset validation split. Refactor model loading and dataset preparation processes for better clarity and robustness. Update trainer configuration to include evaluation strategy and logging of final metrics.

Add training script for SmolLM2-135M model using Unsloth. Includes model loading, dataset preparation, and training configuration. Provides detailed instructions for setup and execution.