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This project demonstrates the steps required to fine-tune the Gemma model for tasks like code generation. We use qLora quantization to reduce memory usage and the SFTTrainer from the trl library for supervised fine-tuning.

Any NLP would require a basic proprocessing to be carried out, inorder to clean the and the Label the dataset on which NLP PreProcessing can be done.