Fine-Tuning

toothfairyai@latest…

Manager for fine-tuning operations.

This manager provides methods to list trainable models, generate datasets, start training, monitor job status, and cancel training jobs.

Example:

>>> client = ToothFairyClient(api_key="...", workspace_id="...")
>>> models = client.finetuning.list_models()
>>> job = client.finetuning.generate_dataset(
...     name="my-sft-job",
...     training_type="conversationalTraining",
...     base_model="toothfairyai/Llama-3.2-1B-Instruct"
... )
>>> status = client.finetuning.get_status(job["training_log_id"])

Accessed via client.finetuning.

Methods

Method	HTTP	Endpoint
`list_models`	GET	`GET /models`
`list_jobs`	GET	`GET /jobs`
`generate_dataset`	POST	`POST /dataset`
`get_status`	GET	`GET /status/{training_log_id}`
`start_training`	POST	`POST /start/{training_log_id}`
`cancel_training`	POST	`POST /cancel/{training_log_id}`
`generate_training_data`	POST	`POST /magicwand`

list_models

List all trainable models with their configurations.

def list_models() -> List[TrainableModel]

Endpoint: GET /models · Fine-Tuning service

Example

client.finetuning.list_models()

list_jobs

List all fine-tuning jobs for the authenticated workspace.

def list_jobs() -> List[TrainingJob]

Endpoint: GET /jobs · Fine-Tuning service

Example

client.finetuning.list_jobs()

generate_dataset

Generate a training dataset from workspace documents.

def generate_dataset(
    name: str,
    base_model: str,
    training_type: str = 'conversationalTraining',
    topics: Optional[List[str]] = None,
    test_size: Optional[float] = None,
    test_size_threshold: Optional[int] = None,
    description: Optional[str] = None
) -> Dict[str, Any]

Endpoint: POST /dataset · Fine-Tuning service

Request fields

Field	Type	Required	Description
`name`	`string`	yes	Human-readable name for the training job
`training_type`	`string`	no	Training method to use. Each method maps to a document type:

conversationalTraining (SFT) — uses Conversation documents
generativeTraining (DPO) — uses Preference documents
grpoTraining (GRPO) — uses Conversation documents (model self-generates)
ktoTraining (KTO) — uses Preference documents (unpaired)
continuedPretraining (CPT) — uses Conversation documents (plain text)

Defaults to conversationalTraining. Note: the REST endpoint does not enforce the enum at request time (validation occurs in the async worker); send one of the listed values.

The Python SDK accepts snake_case keyword arguments and converts them to the camelCase wire fields shown above.

Example

client.finetuning.generate_dataset(name="…", base_model="…")

get_status

Get detailed status of a fine-tuning job.

def get_status(training_log_id: str) -> TrainingStatusResponse

Endpoint: GET /status/{training_log_id} · Fine-Tuning service

Response fields

Field	Type	Description
`training_log_id`	`string`	Training log ID
`status`	`string`	Current job status. Lifecycle: `dispatched` → `inProgress` → `datasetReady` → `fineTuningRequested` → `training` → `completed` (or `inError`).

Example

client.finetuning.get_status(training_log_id="agent-id")

start_training

Start a fine-tuning training job.

def start_training(
    training_log_id: str,
    config: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]

Endpoint: POST /start/{training_log_id} · Fine-Tuning service

Request fields

Field	Type	Required	Description
`config`	`object`	no	Training configuration overrides merged over the existing training configuration. Keys not present retain their prior values.

The Python SDK accepts snake_case keyword arguments and converts them to the camelCase wire fields shown above.

Example

client.finetuning.start_training(training_log_id="agent-id")

cancel_training

Cancel a running fine-tuning job.

def cancel_training(training_log_id: str) -> Dict[str, Any]

Endpoint: POST /cancel/{training_log_id} · Fine-Tuning service

Example

client.finetuning.cancel_training(training_log_id="agent-id")

generate_training_data

Transform a chat conversation or uploaded file into training data.

def generate_training_data(
    chat_id: Optional[str] = None,
    file_key: Optional[str] = None,
    training_type: str = 'conversationalTraining',
    user_id: Optional[str] = None,
    create_document: bool = True,
    system_message: Optional[str] = None,
    instructions: Optional[str] = None,
    title: Optional[str] = None,
    topics: Optional[List[str]] = None,
    status: str = 'draft',
    scope: str = 'training',
    non_preferred_responses: Optional[List[Dict[str, Any]]] = None,
    tools: Optional[List[Dict[str, Any]]] = None,
    generate_non_preferred: bool = False,
    chunk_size: Optional[int] = None,
    overlap_percent: Optional[float] = None,
    num_turns: Optional[int] = None,
    samples_per_chunk: Optional[int] = None
) -> Dict[str, Any]

Endpoint: POST /magicwand · AI service

Request fields

Field	Type	Required	Description
`chatid`	`string`	no	Chat mode: Unique chat session identifier to extract messages from. Mutually exclusive with file_key.
`file_key`	`string`	no	File mode: S3 key of uploaded file to generate training data from. Obtain via `/documents/requestPreSignedURL` endpoint. Mutually exclusive with chatid.

Chat mode: Refine conversation messages. File mode: Guide conversation synthesis from document content.

Examples:

"Focus on technical accuracy"
"Remove any personal information"
"Make the tone more professional"
"Include code examples where relevant" | | title | string | no | Title for the training document (only used when create_document is true) | | topics | array<string> | no | Topic IDs to tag the training document with (only used when create_document is true) | | training_type | string | no | Training method to use

training (default): Document will be used for model training
validation: Document will be used for model validation/evaluation

Allowed: draft, published |

The Python SDK accepts snake_case keyword arguments and converts them to the camelCase wire fields shown above.

Response fields

Field	Type	Description
`success`	`boolean`	Whether the operation completed successfully
`rawTrainingData`	`object`	The transformed conversation in training data format
`messageCount`	`integer`	Total number of messages in the generated training data
`chatId`	`string`	Source chat identifier (chat mode only, null for file mode)
`fileKey`	`string`	Source file S3 key (file mode only, null for chat mode)
`sourceType`	`string`	Indicates whether training data was generated from chat or file

Allowed: conversationalTraining, generativeTraining | | scope | string | The scope of the training document

Example

client.finetuning.generate_training_data()

Fine-Tuning​

Methods​

list_models​

Example​

list_jobs​

Example​

generate_dataset​

Request fields​

Example​

get_status​

Response fields​

Example​

start_training​

Request fields​

Example​

cancel_training​

Example​

generate_training_data​

Request fields​

Response fields​

Example​

Fine-Tuning

Methods

list_models

Example

list_jobs

Example

generate_dataset

Request fields

Example

get_status

Response fields

Example

start_training

Request fields

Example

cancel_training

Example

generate_training_data

Request fields

Response fields

Example