Fine Tuning Qwen 2 with Custom Data

In this tutorial, Mosleh Mahamud demonstrates how to fine-tune the Qwen 2 large language model (LLM) with custom data. Qwen 2, developed by Alibaba Cloud, is a versatile model excelling in various AI tasks and supporting multiple languages. The tutorial is divided into four main steps: data preparation, setting training arguments, training the model, and saving and making inferences. Mosleh begins by installing necessary packages and setting up the environment using Google Colab Pro Plus. He explains the importance of fine-tuning for optimizing model performance and customization for specific tasks. The tutorial covers loading the Qwen 2 model from Hugging Face, preparing the dataset using the Alpaca prompt format, and formatting the data for training. Mosleh demonstrates how to add LoRA adapters to update a small percentage of the model parameters, configure the SFTTrainer with appropriate training arguments, and train the model. Once training is complete, he shows how to save the fine-tuned model locally and push it to the Hugging Face Hub for deployment. The tutorial concludes with a demonstration of making inferences using the fine-tuned model, highlighting the use of the Fast Language Model package for efficient inference. Mosleh encourages viewers to subscribe for more content on LLMs and AI, and invites them to join his Discord server for further discussions.