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API Reference
Fine Tuning
Jobs

Resume fine-tuning

POST/fine_tuning/jobs/{fine_tuning_job_id}/resume

Resume a fine-tune job.

Path ParametersExpand Collapse
fine_tuning_job_id: string
ReturnsExpand Collapse
FineTuningJob = object { id, created_at, error, 16 more }

The fine_tuning.job object represents a fine-tuning job that has been created through the API.

id: string

The object identifier, which can be referenced in the API endpoints.

created_at: number

The Unix timestamp (in seconds) for when the fine-tuning job was created.

error: object { code, message, param }

For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

code: string

A machine-readable error code.

message: string

A human-readable error message.

param: string

The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

fine_tuned_model: string

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: number

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: object { batch_size, learning_rate_multiplier, n_epochs }

The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

batch_size: optional "auto" or number

Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
learning_rate_multiplier: optional "auto" or number

Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
n_epochs: optional "auto" or number

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
model: string

The base model that is being fine-tuned.

object: "fine_tuning.job"

The object type, which is always "fine_tuning.job".

organization_id: string

The organization that owns the fine-tuning job.

result_files: array of string

The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

seed: number

The seed used for the fine-tuning job.

status: "validating_files" or "queued" or "running" or 3 more

The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

Accepts one of the following:
"validating_files"
"queued"
"running"
"succeeded"
"failed"
"cancelled"
trained_tokens: number

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: string

The file ID used for training. You can retrieve the training data with the Files API.

validation_file: string

The file ID used for validation. You can retrieve the validation results with the Files API.

estimated_finish: optional number

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.

integrations: optional array of FineTuningJobWandbIntegrationObject { type, wandb }

A list of integrations to enable for this fine-tuning job.

type: "wandb"

The type of the integration being enabled for the fine-tuning job

wandb: FineTuningJobWandbIntegration { project, entity, name, tags }

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.

project: string

The name of the project that the new run will be created under.

entity: optional string

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.

name: optional string

A display name to set for the run. If not set, we will use the Job ID as the name.

tags: optional array of string

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}".

metadata: optional Metadata

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

method: optional object { type, dpo, reinforcement, supervised }

The method used for fine-tuning.

type: "supervised" or "dpo" or "reinforcement"

The type of method. Is either supervised, dpo, or reinforcement.

Accepts one of the following:
"supervised"
"dpo"
"reinforcement"
dpo: optional DpoMethod { hyperparameters }

Configuration for the DPO fine-tuning method.

hyperparameters: optional DpoHyperparameters { batch_size, beta, learning_rate_multiplier, n_epochs }

The hyperparameters used for the DPO fine-tuning job.

batch_size: optional "auto" or number

Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
beta: optional "auto" or number

The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
learning_rate_multiplier: optional "auto" or number

Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
n_epochs: optional "auto" or number

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
reinforcement: optional ReinforcementMethod { grader, hyperparameters }

Configuration for the reinforcement fine-tuning method.

grader: StringCheckGrader { input, name, operation, 2 more } or TextSimilarityGrader { evaluation_metric, input, name, 2 more } or PythonGrader { name, source, type, image_tag } or 2 more

The grader used for the fine-tuning job.

Accepts one of the following:
StringCheckGrader = object { input, name, operation, 2 more }

A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

input: string

The input text. This may include template strings.

name: string

The name of the grader.

operation: "eq" or "ne" or "like" or "ilike"

The string check operation to perform. One of eq, ne, like, or ilike.

Accepts one of the following:
"eq"
"ne"
"like"
"ilike"
reference: string

The reference text. This may include template strings.

type: "string_check"

The object type, which is always string_check.

TextSimilarityGrader = object { evaluation_metric, input, name, 2 more }

A TextSimilarityGrader object which grades text based on similarity metrics.

evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

Accepts one of the following:
"cosine"
"fuzzy_match"
"bleu"
"gleu"
"meteor"
"rouge_1"
"rouge_2"
"rouge_3"
"rouge_4"
"rouge_5"
"rouge_l"
input: string

The text being graded.

name: string

The name of the grader.

reference: string

The text being graded against.

type: "text_similarity"

The type of grader.

PythonGrader = object { name, source, type, image_tag }

A PythonGrader object that runs a python script on the input.

name: string

The name of the grader.

source: string

The source code of the python script.

type: "python"

The object type, which is always python.

image_tag: optional string

The image tag to use for the python script.

ScoreModelGrader = object { input, model, name, 3 more }

A ScoreModelGrader object that uses a model to assign a score to the input.

input: array of object { content, role, type }

The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

content: string or ResponseInputText { text, type } or object { text, type } or 3 more

Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

Accepts one of the following:
TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }
data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:
"mp3"
"wav"
type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = array of string or ResponseInputText { text, type } or object { text, type } or 2 more

A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

Accepts one of the following:
TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }
data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:
"mp3"
"wav"
type: "input_audio"

The type of the input item. Always input_audio.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:
"user"
"assistant"
"system"
"developer"
type: optional "message"

The type of the message input. Always message.

model: string

The model to use for the evaluation.

name: string

The name of the grader.

type: "score_model"

The object type, which is always score_model.

range: optional array of number

The range of the score. Defaults to [0, 1].

sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

The sampling parameters for the model.

max_completions_tokens: optional number

The maximum number of tokens the grader model may generate in its response.

minimum1
reasoning_effort: optional ReasoningEffort

Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

  • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
  • All models before gpt-5.1 default to medium reasoning effort, and do not support none.
  • The gpt-5-pro model defaults to (and only supports) high reasoning effort.
  • xhigh is supported for all models after gpt-5.1-codex-max.
Accepts one of the following:
"none"
"minimal"
"low"
"medium"
"high"
"xhigh"
seed: optional number

A seed value to initialize the randomness, during sampling.

temperature: optional number

A higher temperature increases randomness in the outputs.

top_p: optional number

An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

MultiGrader = object { calculate_output, graders, name, type }

A MultiGrader object combines the output of multiple graders to produce a single score.

calculate_output: string

A formula to calculate the output based on grader results.

graders: StringCheckGrader { input, name, operation, 2 more } or TextSimilarityGrader { evaluation_metric, input, name, 2 more } or PythonGrader { name, source, type, image_tag } or 2 more

A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

Accepts one of the following:
StringCheckGrader = object { input, name, operation, 2 more }

A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

input: string

The input text. This may include template strings.

name: string

The name of the grader.

operation: "eq" or "ne" or "like" or "ilike"

The string check operation to perform. One of eq, ne, like, or ilike.

Accepts one of the following:
"eq"
"ne"
"like"
"ilike"
reference: string

The reference text. This may include template strings.

type: "string_check"

The object type, which is always string_check.

TextSimilarityGrader = object { evaluation_metric, input, name, 2 more }

A TextSimilarityGrader object which grades text based on similarity metrics.

evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 8 more

The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

Accepts one of the following:
"cosine"
"fuzzy_match"
"bleu"
"gleu"
"meteor"
"rouge_1"
"rouge_2"
"rouge_3"
"rouge_4"
"rouge_5"
"rouge_l"
input: string

The text being graded.

name: string

The name of the grader.

reference: string

The text being graded against.

type: "text_similarity"

The type of grader.

PythonGrader = object { name, source, type, image_tag }

A PythonGrader object that runs a python script on the input.

name: string

The name of the grader.

source: string

The source code of the python script.

type: "python"

The object type, which is always python.

image_tag: optional string

The image tag to use for the python script.

ScoreModelGrader = object { input, model, name, 3 more }

A ScoreModelGrader object that uses a model to assign a score to the input.

input: array of object { content, role, type }

The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

content: string or ResponseInputText { text, type } or object { text, type } or 3 more

Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

Accepts one of the following:
TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }
data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:
"mp3"
"wav"
type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = array of string or ResponseInputText { text, type } or object { text, type } or 2 more

A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

Accepts one of the following:
TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }
data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:
"mp3"
"wav"
type: "input_audio"

The type of the input item. Always input_audio.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:
"user"
"assistant"
"system"
"developer"
type: optional "message"

The type of the message input. Always message.

model: string

The model to use for the evaluation.

name: string

The name of the grader.

type: "score_model"

The object type, which is always score_model.

range: optional array of number

The range of the score. Defaults to [0, 1].

sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }

The sampling parameters for the model.

max_completions_tokens: optional number

The maximum number of tokens the grader model may generate in its response.

minimum1
reasoning_effort: optional ReasoningEffort

Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

  • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
  • All models before gpt-5.1 default to medium reasoning effort, and do not support none.
  • The gpt-5-pro model defaults to (and only supports) high reasoning effort.
  • xhigh is supported for all models after gpt-5.1-codex-max.
Accepts one of the following:
"none"
"minimal"
"low"
"medium"
"high"
"xhigh"
seed: optional number

A seed value to initialize the randomness, during sampling.

temperature: optional number

A higher temperature increases randomness in the outputs.

top_p: optional number

An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

LabelModelGrader = object { input, labels, model, 3 more }

A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

input: array of object { content, role, type }
content: string or ResponseInputText { text, type } or object { text, type } or 3 more

Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

Accepts one of the following:
TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }
data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:
"mp3"
"wav"
type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = array of string or ResponseInputText { text, type } or object { text, type } or 2 more

A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

Accepts one of the following:
TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }
data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:
"mp3"
"wav"
type: "input_audio"

The type of the input item. Always input_audio.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:
"user"
"assistant"
"system"
"developer"
type: optional "message"

The type of the message input. Always message.

labels: array of string

The labels to assign to each item in the evaluation.

model: string

The model to use for the evaluation. Must support structured outputs.

name: string

The name of the grader.

passing_labels: array of string

The labels that indicate a passing result. Must be a subset of labels.

type: "label_model"

The object type, which is always label_model.

name: string

The name of the grader.

type: "multi"

The object type, which is always multi.

hyperparameters: optional ReinforcementHyperparameters { batch_size, compute_multiplier, eval_interval, 4 more }

The hyperparameters used for the reinforcement fine-tuning job.

batch_size: optional "auto" or number

Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
compute_multiplier: optional "auto" or number

Multiplier on amount of compute used for exploring search space during training.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
eval_interval: optional "auto" or number

The number of training steps between evaluation runs.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
eval_samples: optional "auto" or number

Number of evaluation samples to generate per training step.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
learning_rate_multiplier: optional "auto" or number

Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
n_epochs: optional "auto" or number

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
reasoning_effort: optional "default" or "low" or "medium" or "high"

Level of reasoning effort.

Accepts one of the following:
"default"
"low"
"medium"
"high"
supervised: optional SupervisedMethod { hyperparameters }

Configuration for the supervised fine-tuning method.

hyperparameters: optional SupervisedHyperparameters { batch_size, learning_rate_multiplier, n_epochs }

The hyperparameters used for the fine-tuning job.

batch_size: optional "auto" or number

Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
learning_rate_multiplier: optional "auto" or number

Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number
n_epochs: optional "auto" or number

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

Accepts one of the following:
UnionMember0 = "auto"
UnionMember1 = number

Resume fine-tuning

curl https://api.openai.com/v1/fine_tuning/jobs/$FINE_TUNING_JOB_ID/resume \
    -X POST \
    -H "Authorization: Bearer $OPENAI_API_KEY"
{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}
Returns Examples
{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}