> ## Documentation Index > Fetch the complete documentation index at: https://portkey-docs-feat-rerank-documentation.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Create Fine-tuning Job > Finetune a provider model ## OpenAPI ````yaml post /fine_tuning/jobs openapi: 3.0.0 info: title: Portkey API description: >- The Portkey REST API. Please see https://portkey.ai/docs/api-reference for more details. version: 2.0.0 termsOfService: https://portkey.ai/terms contact: name: Portkey Developer Forum url: https://portkey.wiki/community license: name: MIT url: https://github.com/Portkey-AI/portkey-openapi/blob/master/LICENSE servers: - url: https://api.portkey.ai/v1 description: Portkey API Public Endpoint security: - Portkey-Key: [] tags: - name: Assistants description: Build Assistants that can call models and use tools. - name: Audio description: Turn audio into text or text into audio. - name: Chat description: >- Given a list of messages comprising a conversation, the model will return a response. - name: Realtime description: WebSocket proxy for provider Realtime APIs - name: Collections description: Create, List, Retrieve, Update, and Delete collections of prompts. - name: Labels description: Create, List, Retrieve, Update, and Delete labels. - name: Prompt Collections description: Create, List, Retrieve, Update, and Delete prompt collections. - name: PromptPartials description: Create, List, Retrieve, Update, and Delete prompt partials. - name: Prompts description: >- Given a prompt template ID and variables, will run the saved prompt template and return a response. - name: Guardrails description: Create, List, Retrieve, Update, and Delete prompt Guardrails. - name: Completions description: >- Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. - name: Embeddings description: >- Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms. - name: Rerank description: >- Rerank a list of documents based on their relevance to a query. Supported providers include Cohere, Voyage, Jina, Pinecone, Bedrock, and Azure AI. - name: Fine-tuning description: Manage fine-tuning jobs to tailor a model to your specific training data. - name: Batch description: Create large batches of API requests to run asynchronously. - name: Files description: >- Files are used to upload documents that can be used with features like Assistants and Fine-tuning. - name: Images description: Given a prompt and/or an input image, the model will generate a new image. - name: Models description: List and describe the various models available in the API. - name: Moderations description: >- Given a input text, outputs if the model classifies it as potentially harmful. - name: Configs description: Create, List, Retrieve, and Update your Portkey Configs. - name: Feedback description: Send and Update any feedback. - name: Logs description: Custom Logger to add external logs to Portkey. - name: Integrations description: Create, List, Retrieve, Update, and Delete your Portkey Integrations. - name: Integrations > Workspaces description: Manage workspace access for your Portkey Integrations. - name: Integrations > Models description: Manage model access for your Portkey Integrations. - name: Providers description: Create, List, Retrieve, Update, and Delete your Portkey Providers. - name: Virtual-keys description: Create, List, Retrieve, Update, and Delete your Portkey Virtual keys. - name: Users description: Create and manage users. - name: User-invites description: Create and manage user invites. - name: Workspaces description: Create and manage workspaces. - name: Workspaces > Members description: Create and manage workspace members. - name: MCP Integrations description: Create, List, Retrieve, Update, and Delete MCP Integrations. - name: MCP Integrations > Workspaces description: Manage workspace access for MCP Integrations. - name: MCP Integrations > Capabilities description: List and manage capabilities for MCP Integrations. - name: MCP Integrations > Metadata description: Get MCP Integration metadata and sync info. - name: MCP Servers description: >- Create, List, Retrieve, Update, and Delete MCP Servers (workspace instances of MCP Integrations). - name: MCP Servers > Capabilities description: List and manage capabilities for MCP Servers. - name: MCP Servers > User Access description: List and manage user access for MCP Servers. - name: MCP Servers > Connections description: List and manage user connections for MCP Servers. - name: Api-Keys description: Create, List, Retrieve, Update, and Delete your Portkey API keys. - name: Logs Export description: Exports logs service. - name: Audit Logs description: Get audit logs for your Portkey account. - name: Analytics description: >- Get analytics over different data points like requests, costs, tokens, etc. - name: Analytics > Graphs description: Get data points for graphical representation. - name: Analytics > Summary description: Get overall summary for the selected time bucket. - name: Analytics > Groups description: Get grouped metrics for the selected time bucket. - name: Usage Limits Policies description: Manage usage limits policies to control total usage over time - name: Rate Limits Policies description: Manage rate limits policies to control request or token rates - name: Model Pricing description: Model pricing configurations for 2300+ LLMs across 40+ providers - name: Secret-References description: >- Create, List, Retrieve, Update, and Delete secret references to external secret managers. paths: /fine_tuning/jobs: servers: - url: https://api.portkey.ai/v1 description: Portkey API Public Endpoint - url: SELF_HOSTED_GATEWAY_URL description: Self-Hosted Gateway URL post: tags: - Finetune summary: Create a Finetune Job description: Finetune a provider model operationId: createFineTuningJob parameters: [] requestBody: required: true content: application/json: schema: anyOf: - $ref: '#/components/schemas/OpenAIFinetuneJob' - $ref: '#/components/schemas/BedrockFinetuneJob' - $ref: '#/components/schemas/PortkeyFinetuneJob' responses: '200': description: The request has succeeded. content: application/json: schema: $ref: '#/components/schemas/FineTuningJob' security: - Portkey-Key: [] Virtual-Key: [] - Portkey-Key: [] Provider-Auth: [] Provider-Name: [] - Portkey-Key: [] Config: [] - Portkey-Key: [] Provider-Auth: [] Provider-Name: [] Custom-Host: [] x-code-samples: - lang: curl label: Default source: | curl https://api.portkey.ai/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "x-portkey-api-key: $PORTKEY_API_KEY" \ -H "x-portkey-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY" \ -d '{ "training_file": "file-BK7bzQj3FfZFXr7DbL6xJwfo", "model": "gpt-3.5-turbo" }' - lang: python label: Default source: | from portkey_ai import Portkey client = Portkey( api_key = "PORTKEY_API_KEY", virtual_key = "PROVIDER_VIRTUAL_KEY" ) client.fine_tuning.jobs.create( training_file="file-abc123", model="gpt-3.5-turbo" ) - lang: javascript label: Default source: | import Portkey from 'portkey-ai'; const client = new Portkey({ apiKey: 'PORTKEY_API_KEY', virtualKey: 'PROVIDER_VIRTUAL_KEY' }); async function main() { const fineTune = await client.fineTuning.jobs.create({ training_file: "file-abc123" }); console.log(fineTune); } main(); - lang: curl label: Self-hosted source: | curl https://SELF_HOSTED_GATEWAY_URL/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "x-portkey-api-key: $PORTKEY_API_KEY" \ -H "x-portkey-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY" \ -d '{ "training_file": "file-BK7bzQj3FfZFXr7DbL6xJwfo", "model": "gpt-3.5-turbo" }' - lang: python label: Self-hosted source: | from portkey_ai import Portkey client = Portkey( api_key = "PORTKEY_API_KEY", base_url = "SELF_HOSTED_GATEWAY_URL", virtual_key = "PROVIDER_VIRTUAL_KEY" ) client.fine_tuning.jobs.create( training_file="file-abc123", model="gpt-3.5-turbo" ) - lang: javascript label: Self-hosted source: | import Portkey from 'portkey-ai'; const client = new Portkey({ apiKey: 'PORTKEY_API_KEY', baseUrl: 'SELF_HOSTED_GATEWAY_URL', virtualKey: 'PROVIDER_VIRTUAL_KEY' }); async function main() { const fineTune = await client.fineTuning.jobs.create({ training_file: "file-abc123" }); console.log(fineTune); } main(); components: schemas: OpenAIFinetuneJob: type: object description: Gateway supported body params for OpenAI, Azure OpenAI and VertexAI. title: OpenAI Params required: - model - training_file - suffix - method properties: model: type: string description: The base model to finetune training_file: type: string description: The training file to use for the finetune job validation_file: type: string description: The validation file to use for the finetune job suffix: type: string description: The suffix to append to the fine-tuned model name method: type: object properties: type: type: string enum: - supervised - dpo supervised: type: object properties: hyperparameters: type: object properties: n_epochs: type: integer format: int32 learning_rate_multiplier: type: number format: float batch_size: type: integer format: int32 required: - n_epochs - learning_rate_multiplier - batch_size required: - hyperparameters dpo: type: object properties: hyperparameters: type: object properties: n_epochs: type: integer format: int32 learning_rate_multiplier: type: number format: float batch_size: type: integer format: int32 required: - n_epochs - learning_rate_multiplier - batch_size required: - hyperparameters required: - type description: Hyperparameters for the finetune job BedrockFinetuneJob: type: object description: Gateway supported body params for bedrock fine-tuning. title: Bedrock Params properties: job_name: type: string description: Job name for the bedrock finetune job role_arn: type: string description: Role ARN for the bedrock finetune job output_file: type: string description: >- Finetune job's output s3 location, will be constructed based on `training_file` if not provided allOf: - $ref: '#/components/schemas/OpenAIFinetuneJob' PortkeyFinetuneJob: type: object properties: job_name: type: string description: Job name for the bedrock finetune job role_arn: type: string description: Role ARN for the bedrock finetune job output_file: type: string description: >- Finetune job's output s3 location, will be constructed based on `training_file` if not provided portkey_options: allOf: - $ref: '#/components/schemas/PortkeyOptions' description: >- Portkey Gateway Provider specific headers to be passed to the provider, if portkey is used as a provider provider_options: allOf: - $ref: '#/components/schemas/BedrockParams' description: >- Provider specific options to be passed to the provider, optional can be passed directly as well. Can be skipped if same keys are passed at top the level. allOf: - $ref: '#/components/schemas/OpenAIFinetuneJob' description: Gateway supported body params for portkey managed fine-tuning. title: Portkey Params FineTuningJob: type: object title: FineTuningJob description: > The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. properties: id: type: string description: The object identifier, which can be referenced in the API endpoints. created_at: type: integer description: >- The Unix timestamp (in seconds) for when the fine-tuning job was created. error: type: object nullable: true description: >- For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. properties: code: type: string description: A machine-readable error code. message: type: string description: A human-readable error message. param: type: string description: >- The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. nullable: true required: - code - message - param fine_tuned_model: type: string nullable: true description: >- The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. finished_at: type: integer nullable: true description: >- The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running. hyperparameters: type: object description: >- The hyperparameters used for the fine-tuning job. See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning) for more details. properties: n_epochs: oneOf: - type: string enum: - auto - type: integer minimum: 1 maximum: 50 default: auto description: >- The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. "auto" decides the optimal number of epochs based on the size of the dataset. If setting the number manually, we support any number between 1 and 50 epochs. required: - n_epochs model: type: string description: The base model that is being fine-tuned. object: type: string description: The object type, which is always "fine_tuning.job". enum: - fine_tuning.job organization_id: type: string description: The organization that owns the fine-tuning job. result_files: type: array description: >- The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). items: type: string example: file-abc123 status: type: string description: >- The current status of the fine-tuning job, which can be either `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`. enum: - validating_files - queued - running - succeeded - failed - cancelled trained_tokens: type: integer nullable: true description: >- The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running. training_file: type: string description: >- The file ID used for training. You can retrieve the training data with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). validation_file: type: string nullable: true description: >- The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). integrations: type: array nullable: true description: A list of integrations to enable for this fine-tuning job. maxItems: 5 items: oneOf: - $ref: '#/components/schemas/FineTuningIntegration' x-oaiExpandable: true seed: type: integer description: The seed used for the fine-tuning job. estimated_finish: type: integer nullable: true description: >- The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running. required: - created_at - error - finished_at - fine_tuned_model - hyperparameters - id - model - object - organization_id - result_files - status - trained_tokens - training_file - validation_file - seed PortkeyOptions: type: object required: - x-portkey-virtual-key properties: x-portkey-virtual-key: type: string description: The virtual key to communicate with the provider x-portkey-aws-s3-bucket: type: string description: The AWS S3 bucket to use for file upload during finetune x-portkey-vertex-storage-bucket-name: type: string description: Google Storage bucket to use for file upload during finetune example: x-portkey-virtual-key: vkey-1234567890 x-portkey-aws-s3-bucket: my-bucket x-portkey-vertex-storage-bucket-name: my-bucket description: >- Options to be passed to the provider, supports all options supported by the provider from gateway. BedrockParams: type: object properties: job_name: type: string description: Job name for the bedrock finetune job role_arn: type: string description: Role ARN for the bedrock finetune job output_file: type: string description: >- Finetune job's output s3 location, will be constructed based on `training_file` if not provided FineTuningIntegration: type: object title: Fine-Tuning Job Integration required: - type - wandb properties: type: type: string description: The type of the integration being enabled for the fine-tuning job enum: - wandb 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 securitySchemes: Portkey-Key: type: apiKey in: header name: x-portkey-api-key Virtual-Key: type: apiKey in: header name: x-portkey-virtual-key Provider-Auth: type: http scheme: bearer Provider-Name: type: apiKey in: header name: x-portkey-provider Config: type: apiKey in: header name: x-portkey-config Custom-Host: type: apiKey in: header name: x-portkey-custom-host ````