Comprehensive Guide for Instruction Fine-tuning of LLaMa 3.2 using MonsterAPI

This comprehensive guide outlines the process of instruction fine-tuning a Llama-3.2 model to generate code using the Alpaca Python coding dataset with the help of MonsterAPI and the LORA (Learning Over Re-presentation And Similarity Alignment) framework, which preserves pre-trained model knowledge while facilitating its seamless learning of new things. The guide provides a step-by-step approach to fine-tuning the model, including installing necessary dependencies, loading the model tokenizer, preparing the dataset for training, applying the chat template, and pushing the dataset to the Hugging Face hub. Once the dataset is prepared, users can call the MonsterAPI's Fine-Tuning service to fine-tune the model, which takes care of hardware and software requirements internally. The guide encourages users to try out various combinations of fine-tuning using MonsterAPI's Fine-Tuning engine.

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