> ## Documentation Index > Fetch the complete documentation index at: https://developers.telnyx.com/llms.txt > Use this file to discover all available pages before exploring further. # Create a fine tuning job > Create a new fine tuning job. ## OpenAPI ````yaml https://telnyx-openapi-ng.s3.us-east-1.amazonaws.com/ai/inference.yml post /ai/fine_tuning/jobs openapi: 3.1.0 info: title: Telnyx AI Inference API version: 2.0.0 description: API for AI inference, including chat completions, fine-tuning, and models. contact: email: support@telnyx.com servers: - url: https://api.telnyx.com/v2 security: - bearerAuth: [] paths: /ai/fine_tuning/jobs: post: tags: - Fine Tuning summary: Create a fine tuning job description: Create a new fine tuning job. operationId: create_new_finetuningjob_public_finetuning_post requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/CreateFineTuningJobRequest' responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/FineTuningJob' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' x-codeSamples: - lang: JavaScript source: |- import Telnyx from 'telnyx'; const client = new Telnyx({ apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted }); const fineTuningJob = await client.ai.fineTuning.jobs.create({ model: 'model', training_file: 'training_file', }); console.log(fineTuningJob.id); - lang: Python source: |- import os from telnyx import Telnyx client = Telnyx( api_key=os.environ.get("TELNYX_API_KEY"), # This is the default and can be omitted ) fine_tuning_job = client.ai.fine_tuning.jobs.create( model="model", training_file="training_file", ) print(fine_tuning_job.id) - lang: Go source: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/team-telnyx/telnyx-go\"\n\t\"github.com/team-telnyx/telnyx-go/option\"\n)\n\nfunc main() {\n\tclient := telnyx.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tfineTuningJob, err := client.AI.FineTuning.Jobs.New(context.TODO(), telnyx.AIFineTuningJobNewParams{\n\t\tModel: \"model\",\n\t\tTrainingFile: \"training_file\",\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", fineTuningJob.ID)\n}\n" - lang: Java source: |- package com.telnyx.sdk.example; import com.telnyx.sdk.client.TelnyxClient; import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient; import com.telnyx.sdk.models.ai.finetuning.jobs.FineTuningJob; import com.telnyx.sdk.models.ai.finetuning.jobs.JobCreateParams; public final class Main { private Main() {} public static void main(String[] args) { TelnyxClient client = TelnyxOkHttpClient.fromEnv(); JobCreateParams params = JobCreateParams.builder() .model("model") .trainingFile("training_file") .build(); FineTuningJob fineTuningJob = client.ai().fineTuning().jobs().create(params); } } - lang: Ruby source: >- require "telnyx" telnyx = Telnyx::Client.new(api_key: "My API Key") fine_tuning_job = telnyx.ai.fine_tuning.jobs.create(model: "model", training_file: "training_file") puts(fine_tuning_job) - lang: PHP source: >- ai->fineTuning->jobs->create( model: 'model', trainingFile: 'training_file', hyperparameters: ['nEpochs' => 1], suffix: 'suffix', ); var_dump($fineTuningJob); } catch (APIException $e) { echo $e->getMessage(); } - lang: CLI source: |- telnyx ai:fine-tuning:jobs create \ --api-key 'My API Key' \ --model model \ --training-file training_file components: schemas: CreateFineTuningJobRequest: properties: model: type: string description: The base model that is being fine-tuned. training_file: type: string description: The storage bucket or object used for training. suffix: type: string description: Optional suffix to append to the fine tuned model's name. hyperparameters: type: object description: The hyperparameters used for the fine-tuning job. properties: n_epochs: type: integer minimum: 1 maximum: 10 default: 3 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. type: object required: - model - training_file title: CreateFineTuningJobRequest 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 name of the fine-tuned model that is being created. created_at: type: integer description: >- The Unix timestamp (in seconds) for when the fine-tuning job was created. finished_at: type: - integer - 'null' 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. properties: n_epochs: type: integer minimum: 1 maximum: 10 default: 3 description: >- The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. required: - n_epochs model: type: string description: The base model that is being fine-tuned. organization_id: type: string description: The organization that owns the fine-tuning job. status: type: string description: The current status of the fine-tuning job. enum: - queued - running - succeeded - failed - cancelled trained_tokens: type: - integer - 'null' 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 storage bucket or object used for training. required: - created_at - finished_at - hyperparameters - id - model - organization_id - status - trained_tokens - training_file HTTPValidationError: title: HTTPValidationError type: object properties: detail: title: Detail type: array items: $ref: '#/components/schemas/ValidationError' ValidationError: title: ValidationError required: - loc - msg - type type: object properties: loc: title: Location type: array items: anyOf: - type: string - type: integer msg: title: Message type: string type: title: Error Type type: string securitySchemes: bearerAuth: scheme: bearer type: http ````