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Projects

Within an organization, people can create several projects. Typically, a project represents a single model they are trying to fine-tune, but it doesn’t have to be limited to this use case. Projects provide a way to organize and manage different initiatives or goals within the organization. Each project can have its own set of datasets, allowing for multiple strategies of curating datasets for a single model. This structure enables teams to compartmentalize their work and manage multiple fine-tuning efforts simultaneously.

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Datasets

Within a project, there can be several datasets. Each dataset is a subset of an organization’s samples that a person wants to generate a fine-tuning dataset from. Datasets allow users to experiments with their curation process and organize specific collections of samples for different model variants. A key feature of datasets in Alchemist is version control. When you complete a dataset, you can still edit it by adding and removing samples from the set. Version control enables users to track changes, revert to previous versions if needed, and maintain a history of dataset iterations.