The Repositories API provides methods for creating and retrieving adapter
repositories, which are used to organize and track your fine-tuning experiments.
pb.repos.create( name: str, # Name for the repository description: str = None, # Description for the repository exists_ok: bool = False # If True, doesn't raise error if exists) -> Repository
Create a new adapter repository to track your fine-tuning experiments.Parameters
name: str - Name for the repository
description: str, optional - Description for the repository
exists_ok: bool, default False - If True, doesn’t raise an error if the
repository already exists
Returns
Repository - The created repository object
Example 1: Create a new repository
Copy
Ask AI
# Create a new adapter repositoryrepo = pb.repos.create( name="text-summarization", description="Experiments for fine-tuning summarization models")
Example 2: Create or get existing repository
Copy
Ask AI
# Create a repository or get it if it already existsrepo = pb.repos.create( name="customer-support-bot", description="Models for customer support automation", exists_ok=True)
pb.repos.get( name: str # Name of the repository) -> Repository
Fetch an adapter repository by name.Parameters
name: str - Name of the repository
Returns
Repository - The requested repository object
Example
Copy
Ask AI
# Get a repository by namerepo = pb.repos.get("text-summarization")# Print repository detailsprint(f"Repository: {repo.name}")print(f"Description: {repo.description}")
After you’ve created or retrieved a repository, you can use it to manage
adapters.Create an Adapter in a Repository
Copy
Ask AI
from predibase import SFTConfig# Create a repositoryrepo = pb.repos.create(name="my-experiments", description="My fine-tuning experiments", exists_ok=True)# Create an adapter in the repositoryadapter = pb.adapters.create( config=SFTConfig( base_model="qwen3-8b" ), dataset="my-dataset", repo=repo.name, # Use the repository name description="First fine-tuning experiment")