PATCH /v1/finetuning/finetuned-models/{id}

Servers

https://api.cohere.com

Path parameters

Name	Type	Required	Description
`id`	String	Yes	FinetunedModel ID.

Request headers

Name	Type	Required	Description
`Content-Type`	String	Yes	The media type of the request body. Default value: "application/json"
`X-Client-Name`	String	No	The name of the project that is making the request.

Request body fields

Name	Type	Required	Description
`name`	String	Yes	FinetunedModel name (e.g. `foobar`).
`creator_id`	String	No	User ID of the creator.
`settings`	Object	Yes	FinetunedModel settings such as dataset, hyperparameters...
`settings.multi_label`	Boolean	No	read-only. Whether the model is single-label or multi-label (only for classification).
`settings.dataset_id`	String	Yes	The data used for training and evaluating the fine-tuned model.
`settings.hyperparameters`	Object	No	Fine-tuning hyper-parameters.
`settings.hyperparameters.train_epochs`	Integer	No	The number of epochs to train for.
`settings.hyperparameters.lora_rank`	Integer	No	Specifies the rank for low-rank matrices. Lower ranks reduce parameters but may limit model flexibility.
`settings.hyperparameters.lora_alpha`	Integer	No	Controls the scaling factor for LoRA updates. Higher values make the updates more impactful.
`settings.hyperparameters.early_stopping_threshold`	Number	No	How much the loss must improve to prevent early stopping.
`settings.hyperparameters.learning_rate`	Number	No	The learning rate to be used during training.
`settings.hyperparameters.lora_target_modules`	String	No	The combination of LoRA modules to target. Possible values: `"LORA_TARGET_MODULES_QKVO"` `"LORA_TARGET_MODULES_QV"` `"LORA_TARGET_MODULES_QKVO_FFN"` `"LORA_TARGET_MODULES_UNSPECIFIED"` Default value: "LORA_TARGET_MODULES_UNSPECIFIED"
`settings.hyperparameters.early_stopping_patience`	Integer	No	Stops training if the loss metric does not improve beyond the value of `early_stopping_threshold` after this many times of evaluation.
`settings.hyperparameters.train_batch_size`	Integer	No	The batch size is the number of training examples included in a single training pass.
`settings.base_model`	Object	Yes	The base model to fine-tune.
`settings.base_model.name`	String	No	The name of the base model.
`settings.base_model.base_type`	String	Yes	The type of the base model. Possible values: `"BASE_TYPE_GENERATIVE"` `"BASE_TYPE_UNSPECIFIED"` `"BASE_TYPE_RERANK"` `"BASE_TYPE_CLASSIFICATION"` `"BASE_TYPE_CHAT"` Default value: "BASE_TYPE_UNSPECIFIED"
`settings.base_model.strategy`	String	No	Deprecated: The fine-tuning strategy. Possible values: `"STRATEGY_UNSPECIFIED"` `"STRATEGY_TFEW"` `"STRATEGY_VANILLA"` Default value: "STRATEGY_UNSPECIFIED"
`settings.base_model.version`	String	No	read-only. The version of the base model.
`settings.wandb`	Object	No	The Weights & Biases configuration (Chat fine-tuning only).
`settings.wandb.api_key`	String	Yes	The WandB API key to be used during training.
`settings.wandb.entity`	String	No	The WandB entity name to be used during training.
`settings.wandb.project`	String	Yes	The WandB project name to be used during training.
`status`	String	No	Current stage in the life-cycle of the fine-tuned model. Possible values: `"STATUS_FAILED"` `"STATUS_TEMPORARILY_OFFLINE"` `"STATUS_DELETED"` `"STATUS_READY"` `"STATUS_PAUSED"` `"STATUS_UNSPECIFIED"` `"STATUS_FINETUNING"` `"STATUS_DEPLOYING_API"` `"STATUS_QUEUED"` Default value: "STATUS_UNSPECIFIED"
`completed_at`	String	No	Timestamp for the completed fine-tuning.
`last_used`	String	No	Deprecated: Timestamp for the latest request to this fine-tuned model.
`organization_id`	String	No	Organization ID.
`created_at`	String	No	Creation timestamp.
`updated_at`	String	No	Latest update timestamp.

How to start integrating

Add HTTP Task to your workflow definition.
Search for the API you want to integrate with and click on the name.
- This loads the API reference documentation and prepares the Http request settings.
Click Test request to test run your request to the API and see the API's response.