How to Fine-Tune Qwen-2 for Free | Open-Source LLM Guide

In this video, the focus is on fine-tuning the Qwen-2 model, a powerful 7 billion parameter language model with 28 layers, to perform text classification. Specifically, the task involves classifying news articles into one of four categories: 'world,' 'sports,' 'business,' and 'sci/tech.' The notebook guides through the entire process, starting from tokenizing the dataset and applying the appropriate model inputs, to training the model using a free version of a 16GB GPU. The fine-tuning process leverages a dataset containing 7 trillion tokens, ensuring that the model can handle a wide range of tasks, including multilingual understanding and coding.
The importance of this video lies in its demonstration of how open-source LLMs like Qwen-2 can be effectively fine-tuned to achieve high performance on specific tasks, even when computational resources are limited. This process not only makes the model more versatile and adaptable to various applications but also highlights the growing demand for fine-tuning skills in the industry. By sharing this practical example, the notebook and the corresponding video aim to empower researchers and developers to unlock the full potential of open-source models in their projects.

[@Ssingh-lz4jc]

Thanks for sharing 🙏

[@MachineLearningSchool]

Link to notebook: drive.google.com/file/d/1evx24o1tN33HAb5eI-hFsQtez1VivdDo/view?usp=sharing

[@mandeepusakaur]

Nice 👍