Fine-Tuning Using The Bakery: Step-by-Step Guide
Last updated
Last updated
This guide will walk you through the process of fine-tuning a model using datasets in The Bakery.
Start by navigating to the top of your screen and selecting Create Dataset.
Next, choose a name for your dataset, assign a category and tags, and select Raw Dataset as the dataset type.
A raw dataset contains unprocessed data that will be used to train and fine-tune your model. Unlike a vector dataset, which is optimized for querying or searching, raw datasets are essential for fine-tuning because they contain detailed training data without any preprocessing.
Once the dataset is created, it will be labeled with the special identifier RAW.
Now, navigate to the newly created dataset and upload the file for the fine-tuning process. In this example, we are using a file in Parquet format.
While in the Files section, locate the Fine Tune option on the right-hand side of your screen.
You can now begin the fine-tuning process by filling in the required details. For this tutorial, we’ll be using a base model from the marketplace. For more information on purchasing and listing models or datasets on The Bakery, refer to the documentation.
Next, select the Raw Dataset we created earlier, which should contain the training data.
Create a name for the fine-tuned model.
Choose the file from the Raw Dataset that you want to use for training.
After you’ve submitted your inputs, click Confirm Fine Tune and proceed with the transaction for the fine-tuning process.
A progress bar will appear, allowing you to monitor the fine-tuning process.
Once the fine-tuning is complete, you can download the fine-tuned model files by navigating to the Files section and selecting Download All.
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