In the video, the presenter addresses feedback on a previous tutorial about fine-tuning models using MLX, clarifying the dataset creation process and the significance of different data files, including train.jsonl, valid.jsonl, and test.jsonl. The discussion also covers the differences between JSON and JSONL formats, and the presenter outlines the steps for fine-tuning the model while inviting viewers to share their experiences.
In the video, the presenter addresses feedback received from a previous tutorial on fine-tuning models using MLX, specifically focusing on a Mac-only solution. The presenter acknowledges that while the last video was well-received, there were several comments highlighting issues, such as the lack of code for creating the dataset and examples of its format. The presenter aims to clarify these points and improve the upcoming video on UNS Sloth by discussing the dataset creation process, the importance of different data files, and the benefits of using JSON Lines (JSONL) format.
The presenter begins by explaining the dataset creation process, noting that the initial approach involved formatting the data according to the specifications on the model’s page on Hugging Face. However, it was suggested that a simpler method using objects with prompt and completion keys could be employed. Despite attempts to implement this suggestion, the presenter encountered issues due to the need for additional modules and configuration files, which led to the conclusion that using text keys only is currently the most viable option.
Next, the video delves into the purpose of the three generated files: train.jsonl, valid.jsonl, and test.jsonl. The train file is used for the actual fine-tuning of the model, while the valid file serves to evaluate the model’s performance on unseen data. The test file, although not utilized during training, is created automatically and may be useful for future testing purposes. The presenter emphasizes the importance of understanding these distinctions for effective model training.
The discussion then shifts to the differences between JSON and JSONL formats. Initially, the presenter believed JSONL was necessary for handling larger files, but later realized that libraries exist for managing large JSON files in various programming languages. The presenter acknowledges that while JSONL may offer some advantages, the choice of format is often a matter of preference and practicality rather than a strict requirement.
Finally, the video outlines the steps for fine-tuning the model using the MLX command, highlighting the importance of downloading the model first and running the command with the correct parameters. The presenter demonstrates how to create a model file and quantize it in one step, noting that while the results may not be perfect due to the dataset’s quality, the process is straightforward. The video concludes with an invitation for viewers to share their fine-tuning experiences and encourages them to subscribe for future content.