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Building applications with LLMs through composability in Kotlin
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/**
 *
 * Please note:
 * This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
 * Do not edit this file manually.
 *
 */

@file:Suppress(
    "ArrayInDataClass",
    "EnumEntryName",
    "RemoveRedundantQualifierName",
    "UnusedImport"
)

package com.xebia.functional.openai.generated.model

import com.xebia.functional.openai.generated.model.CreateFineTuningJobRequestHyperparameters
import com.xebia.functional.openai.generated.model.CreateFineTuningJobRequestIntegrationsInner
import com.xebia.functional.openai.generated.model.CreateFineTuningJobRequestModel



import kotlinx.serialization.Serializable
import kotlinx.serialization.SerialName
import kotlinx.serialization.Contextual
import kotlin.js.JsName
import kotlinx.serialization.json.*

/**
* 
*
    * @param model 
    * @param trainingFile 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`.  See the [fine-tuning guide](/docs/guides/fine-tuning) for more details. 
    * @param hyperparameters 
    * @param suffix A string of up to 18 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-3.5-turbo:openai:custom-model-name:7p4lURel`. 
    * @param validationFile 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. 
    * @param integrations A list of integrations to enable for your fine-tuning job.
    * @param seed 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. 
*/
@Serializable


data class CreateFineTuningJobRequest (
        @SerialName(value = "model") val model: CreateFineTuningJobRequestModel,
        /* 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`.  See the [fine-tuning guide](/docs/guides/fine-tuning) for more details.  */
    @SerialName(value = "training_file") val trainingFile: kotlin.String,
        @SerialName(value = "hyperparameters") val hyperparameters: CreateFineTuningJobRequestHyperparameters? = null,
        /* A string of up to 18 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-3.5-turbo:openai:custom-model-name:7p4lURel`.  */
    @SerialName(value = "suffix") val suffix: kotlin.String? = null,
        /* 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.  */
    @SerialName(value = "validation_file") val validationFile: kotlin.String? = null,
        /* A list of integrations to enable for your fine-tuning job. */
    @SerialName(value = "integrations") val integrations: kotlin.collections.List? = null,
        /* 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.  */
    @SerialName(value = "seed") val seed: kotlin.Int? = null
) {

}