The Adapters API provides methods for creating, retrieving, and managing adapters for fine-tuned models.

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Create Adapter (Finetune)

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Fine-Tune Adapter

pb.adapters.create(
    config: SFTConfig | ContinuedPretrainingConfig | GRPOConfig,       # Configuration for fine-tuning
    dataset: str,                                                      # The dataset to use for fine-tuning
    repo: str,                                                         # Name of the adapter repo
    continue_from_version: str = None,                                 # The adapter version to continue training from
    description: str = None,                                           # Description for the adapter
    show_tensorboard: bool = False                                     # If true, launch tensorboard instance
) -> Adapter

Create a new adapter by starting a new (blocking) fine-tuning job.

Parameters

  • config: SFTConfig | ContinuedPretrainingConfig | GRPOConfig - Configuration for fine-tuning
  • dataset: str - The dataset to use for fine-tuning
  • continue_from_version: str, optional - The adapter version to continue training from
  • repo: str - Name of the adapter repo to store the newly created adapter
  • description: str, optional - Description for the adapter
  • show_tensorboard: bool, default False - If true, launch a tensorboard instance to view training logs

Returns

  • Adapter - The created adapter object

Example 1: Create an adapter with defaults

# Create an adapter repository
repo = pb.repos.create(name="news-summarizer-model", description="TLDR News Summarizer Experiments", exists_ok=True)

# Start a fine-tuning job, blocks until training is finished
adapter = pb.adapters.create(
    config=SFTConfig(
        base_model="qwen3-8b"
    ),
    dataset="tldr_news",
    repo=repo,
    description="initial model with defaults"
)

Example 2: Create a new adapter with customized parameters

# Create an adapter repository
repo = pb.repos.create(name="news-summarizer-model", description="TLDR News Summarizer Experiments", exists_ok=True)

# Start a fine-tuning job with custom parameters, blocks until training is finished
adapter = pb.adapters.create(
    config=SFTConfig(
        base_model="qwen3-8b",
        task="instruction_tuning",
        epochs=1, # default: 3
        rank=8, # default: 16
        learning_rate=0.0001, # default: 0.0002
        target_modules=["q_proj", "v_proj", "k_proj"], # default: None (infers [q_proj, v_proj] for qwen3-8b)
    ),
    dataset="tldr_news",
    repo=repo,
    description="changing epochs, rank, and learning rate"
)

Example 3: Continue training from an existing adapter

adapter = pb.adapters.create(
    # Note: only `epochs` and `enable_early_stopping` are available parameters in this case.
    config=SFTConfig(
        epochs=3,  # The maximum number of ADDITIONAL epochs to train for
        enable_early_stopping=False,
    ),
    continue_from_version="myrepo/3",  # The adapter version to resume training from
    dataset="mydataset",
    repo="myrepo"
)

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Fine-Tune Adapter (Async)

pb.finetuning.jobs.create(
    config: SFTConfig | ContinuedPretrainingConfig | GRPOConfig,     # Configuration for fine-tuning
    dataset: str,                                                    # The dataset to use for fine-tuning
    repo: str,                                                       # Name of the adapter repo
    continue_from_version: str = None,                               # The adapter version to continue training from
    description: str = None,                                         # Description for the adapter
    watch: bool = False,                                             # Whether to block until job finishes
    show_tensorboard: bool = False                                   # If true, launch tensorboard instance
) -> FinetuningJob

Start a non-blocking fine-tuning job for creating an adapter.

Parameters

  • config: SFTConfig | ContinuedPretrainingConfig | GRPOConfig - Configuration for fine-tuning
  • dataset: str - The dataset to use for fine-tuning
  • repo: str - Name of the adapter repo to store the newly created adapter
  • continue_from_version: str, optional - The adapter version to continue training from
  • description: str, optional - Description for the adapter
  • watch: bool, default False - Whether to block until the fine-tuning job finishes
  • show_tensorboard: bool, default False - Whether to launch a tensorboard instance

Returns

  • FinetuningJob - Object representing the fine-tuning job

Example

adapter_job = pb.finetuning.jobs.create(
    config=SFTConfig(
        base_model="qwen3-8b",
        task="instruction_tuning",
        epochs=1,
        rank=8,
        target_modules=["q_proj", "v_proj", "k_proj"],
    ),
    dataset="tldr_news",
    repo="news-summarizer-model",
    description="async job example",
    watch=False,
    show_tensorboard=False,
)

All the examples for the synchronous pb.adapters.create method are also valid for the asynchronous pb.finetuning.jobs.create method.

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Get Adapter

pb.adapters.get(
    adapter_id: str,     # Name of the adapter in the format "repo/version"
) -> Adapter

Fetch an adapter by name and version.

Parameters

  • adapter_id: str - ID of the adapter in the format “repo/version”

Returns

  • Adapter - The requested adapter object

Example

# Get an adapter by ID
adapter = pb.adapters.get("news-summarizer-model/1")

# Print adapter details
print(f"Adapter: {adapter.name} v{adapter.tag}")
print(f"Description: {adapter.description}")
print(f"Base Model: {adapter.base_model}")

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Upload Adapter

pb.adapters.upload(
    local_dir: str,     # Path to the local directory containing the adapter files
    repo: str,          # Name of the adapter repo
) -> Adapter

Upload existing adapter weights to create a new adapter version.

Parameters

  • local_dir: str - Path to the local directory containing the adapter files
  • repo: str - Name of the adapter repo

Returns

  • Adapter - The uploaded adapter object

Example

# Upload adapter weights
adapter = pb.adapters.upload(
    local_dir="./my_adapter_weights",
    repo="news-summarizer-model",
)
print(f"Uploaded adapter: {adapter.name} v{adapter.tag}")

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Cancel Adapter (Finetune)

pb.adapters.cancel(
    adapter_id: str,     # Name of the adapter in the format "repo/version"
) -> None

Cancel a running fine-tuning job for an adapter.

Parameters

  • adapter_id: str - ID of the adapter in the format “repo/version”

Returns

  • None - The fine-tuning job is cancelled

Example

pb.adapters.cancel("news-summarizer-model/1")

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Download Adapter

pb.adapters.download(
    adapter_id: str,             # Name of the adapter in the format "repo/version"
    dest: os.PathLike = None,    # Local destination to download the weights to
) -> None

Download the adapter weights of an existing adapter.

Parameters

  • adapter_id: str - ID of the adapter in the format “repo/version”
  • dest: os.PathLike, optional - Local destination to download the weights

Returns

  • None - The adapter weights are downloaded to the local destination

Example

# Download adapter weights
pb.adapters.download(
    adapter_id="news-summarizer-model/1",
    dest="./adapter_weights/"
)

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Archive Adapter

pb.adapters.archive(
    adapter_id: str,     # Name of the adapter in the format "repo/version"
) -> None

Archive an adapter version to hide it in the UI.

Parameters

  • adapter_id: str - ID of the adapter in the format “repo/version”

Returns

  • None - The adapter version is archived

Example

# Archive an adapter
pb.adapters.archive("news-summarizer-model/1")

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Unarchive Adapter

pb.adapters.unarchive(
    adapter_id: str,     # Name of the adapter in the format "repo/version"
) -> None

Unarchive an adapter version to make it visible in the UI.

Parameters

  • adapter_id: str - ID of the adapter in the format “repo/version”

Returns

  • None - The adapter version is unarchived

Example

# Unarchive an adapter
pb.adapters.unarchive("news-summarizer-model/1")

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Delete Adapter

pb.adapters.delete(
    adapter_id: str,     # Name of the adapter in the format "repo/version"
) -> None

Delete an adapter version.

Parameters

  • adapter_id: str - ID of the adapter in the format “repo/version”

Returns

  • None - The adapter version is deleted

Example

# Delete an adapter
pb.adapters.delete("news-summarizer-model/1")