Customers can kick off fine-tuning jobs by completing the data preparation and validation steps through the Web UI. This is useful for customers who don't need or don't want to create a fine-tuning job programatically via the Cohere Python SDK, instead preferring the ease and simplicity of a web interface.
Before a fine-tune job can be started, users must upload a dataset with training and (optionally) evaluation data. The contents and structure of the dataset will vary depending on the type of fine-tuning. Read more about preparing the training data for Generate, Chat, Classify, and Rerank fine-tuning.
After uploading the dataset and going through the validation and review data phases in the UI, the fine-tune job can begin. Read more about starting the fine-tuning jobs for Generate, Chat, Classify, and Rerank.
You will receive an email notification when the fine-tune model is ready. You can explore the evaluation metrics using the Dashboard Web UI and try out your model using one of our APIs on the interactive Playground.
As your fine-tune job progresses, it will progress through various stages. The following table describes the meaning of the various status messages you might encounter:
|Queued||Fine-tune job is queued to start.|
|Created||Fine-tune job has been created and will start training soon.|
|Training||Fine-tune job is currently training.|
|Deploying||Fine-tune job has finished training and is deploying the model endpoint.|
|Ready||Fine-tune job has finished and is ready to be called.|
|Asleep||Fine-tune job has been paused. To wake the model, you can select the "Wake Model" button on the Status tab.|
|Failed||Fine-tune job has failed. Please contact customer support if you need more help in understanding why your fine-tune has failed.|
Updated 7 days ago