--- title: "CreateFineTuningJobRequest" url: "https://kongair.terwilligar.com/apis/openai-api-2-3-0/versions/ea92d048-c746-4ead-a6a8-da28d93137d1/schemas/CreateFineTuningJobRequest" --- > Full API specification: https://kongair.terwilligar.com/apis/openai-api-2-3-0/versions/ea92d048-c746-4ead-a6a8-da28d93137d1.md # CreateFineTuningJobRequest ## OpenAPI definition ```yaml openapi: 3.0.0 info: title: OpenAI API version: 2.3.0 servers: - url: https://api.openai.com/v1 components: schemas: FineTuneMethod: type: object description: The method used for fine-tuning. properties: type: type: string description: The type of method. Is either `supervised` or `dpo`. enum: - supervised - dpo supervised: $ref: "#/components/schemas/FineTuneSupervisedMethod" dpo: $ref: "#/components/schemas/FineTuneDPOMethod" FineTuneSupervisedMethod: type: object description: Configuration for the supervised fine-tuning method. properties: hyperparameters: type: object description: The hyperparameters used for the fine-tuning job. properties: batch_size: description: > Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. oneOf: - type: string enum: - auto x-stainless-const: true - type: integer minimum: 1 maximum: 256 default: auto learning_rate_multiplier: description: > Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. oneOf: - type: string enum: - auto x-stainless-const: true - type: number minimum: 0 exclusiveMinimum: true default: auto n_epochs: description: > The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. oneOf: - type: string enum: - auto x-stainless-const: true - type: integer minimum: 1 maximum: 50 default: auto FineTuneDPOMethod: type: object description: Configuration for the DPO fine-tuning method. properties: hyperparameters: type: object description: The hyperparameters used for the fine-tuning job. properties: beta: description: > The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. oneOf: - type: string enum: - auto x-stainless-const: true - type: number minimum: 0 maximum: 2 exclusiveMinimum: true default: auto batch_size: description: > Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. oneOf: - type: string enum: - auto x-stainless-const: true - type: integer minimum: 1 maximum: 256 default: auto learning_rate_multiplier: description: > Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. oneOf: - type: string enum: - auto x-stainless-const: true - type: number minimum: 0 exclusiveMinimum: true default: auto n_epochs: description: > The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. oneOf: - type: string enum: - auto x-stainless-const: true - type: integer minimum: 1 maximum: 50 default: auto CreateFineTuningJobRequest: type: object properties: model: description: > The name of the model to fine-tune. You can select one of the [supported models](/docs/guides/fine-tuning#which-models-can-be-fine-tuned). example: gpt-4o-mini anyOf: - type: string - type: string enum: - babbage-002 - davinci-002 - gpt-3.5-turbo - gpt-4o-mini x-oaiTypeLabel: string training_file: description: > The ID of an uploaded file that contains training data. See [upload file](/docs/api-reference/files/create) for how to upload a file. Your dataset must be formatted as a JSONL file. Additionally, you must upload your file with the purpose `fine-tune`. The contents of the file should differ depending on if the model uses the [chat](/docs/api-reference/fine-tuning/chat-input), [completions](/docs/api-reference/fine-tuning/completions-input) format, or if the fine-tuning method uses the [preference](/docs/api-reference/fine-tuning/preference-input) format. See the [fine-tuning guide](/docs/guides/fine-tuning) for more details. type: string example: file-abc123 hyperparameters: type: object description: > The hyperparameters used for the fine-tuning job. This value is now deprecated in favor of `method`, and should be passed in under the `method` parameter. properties: batch_size: description: > Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. oneOf: - type: string enum: - auto x-stainless-const: true - type: integer minimum: 1 maximum: 256 default: auto learning_rate_multiplier: description: > Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. oneOf: - type: string enum: - auto x-stainless-const: true - type: number minimum: 0 exclusiveMinimum: true default: auto n_epochs: description: > The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. oneOf: - type: string enum: - auto x-stainless-const: true - type: integer minimum: 1 maximum: 50 default: auto deprecated: true suffix: description: > A string of up to 64 characters that will be added to your fine-tuned model name. For example, a `suffix` of "custom-model-name" would produce a model name like `ft:gpt-4o-mini:openai:custom-model-name:7p4lURel`. type: string minLength: 1 maxLength: 64 default: null nullable: true validation_file: description: > The ID of an uploaded file that contains validation data. If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the fine-tuning results file. The same data should not be present in both train and validation files. Your dataset must be formatted as a JSONL file. You must upload your file with the purpose `fine-tune`. See the [fine-tuning guide](/docs/guides/fine-tuning) for more details. type: string nullable: true example: file-abc123 integrations: type: array description: A list of integrations to enable for your fine-tuning job. nullable: true items: type: object required: - type - wandb properties: type: description: > The type of integration to enable. Currently, only "wandb" (Weights and Biases) is supported. oneOf: - type: string enum: - wandb x-stainless-const: true wandb: type: object description: > The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run. required: - project properties: project: description: > The name of the project that the new run will be created under. type: string example: my-wandb-project name: description: > A display name to set for the run. If not set, we will use the Job ID as the name. nullable: true type: string entity: description: > The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used. nullable: true type: string tags: description: > A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". type: array items: type: string example: custom-tag seed: description: > The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you. type: integer nullable: true minimum: 0 maximum: 2147483647 example: 42 method: $ref: "#/components/schemas/FineTuneMethod" required: - model - training_file ```