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POST
/
v1
/
accounts
/
{account_id}
/
reinforcementFineTuningJobs
/
{reinforcement_fine_tuning_job_id}
:resume
Resume Reinforcement Fine-tuning Job
curl --request POST \
  --url https://api.fireworks.ai/v1/accounts/{account_id}/reinforcementFineTuningJobs/{reinforcement_fine_tuning_job_id}:resume \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{}'
{
  "dataset": "<string>",
  "evaluator": "<string>",
  "name": "<string>",
  "displayName": "<string>",
  "createTime": "2023-11-07T05:31:56Z",
  "completedTime": "2023-11-07T05:31:56Z",
  "evaluationDataset": "<string>",
  "evalAutoCarveout": true,
  "state": "JOB_STATE_UNSPECIFIED",
  "status": {
    "code": "OK",
    "message": "<string>"
  },
  "createdBy": "<string>",
  "trainingConfig": {
    "outputModel": "<string>",
    "baseModel": "<string>",
    "warmStartFrom": "<string>",
    "jinjaTemplate": "<string>",
    "learningRate": 123,
    "maxContextLength": 123,
    "loraRank": 123,
    "region": "REGION_UNSPECIFIED",
    "epochs": 123,
    "batchSize": 123,
    "gradientAccumulationSteps": 123,
    "learningRateWarmupSteps": 123
  },
  "wandbConfig": {
    "enabled": true,
    "apiKey": "<string>",
    "project": "<string>",
    "entity": "<string>",
    "runId": "<string>",
    "url": "<string>"
  },
  "outputStats": "<string>",
  "inferenceParameters": {
    "maxOutputTokens": 123,
    "temperature": 123,
    "topP": 123,
    "responseCandidatesCount": 123,
    "extraBody": "<string>",
    "topK": 123
  },
  "chunkSize": 123,
  "outputMetrics": "<string>",
  "mcpServer": "<string>",
  "nodeCount": 123,
  "lossConfig": {
    "method": "METHOD_UNSPECIFIED",
    "klBeta": 123
  }
}

Authorizations

​
Authorization
string
header
required

Bearer authentication using your Fireworks API key. Format: Bearer <API_KEY>

Path Parameters

​
account_id
string
required

The Account Id

​
reinforcement_fine_tuning_job_id
string
required

The Reinforcement Fine-tuning Job Id

Body

application/json

The body is of type object.

Response

200 - application/json

A successful response.

​
dataset
string
required

The name of the dataset used for training.

​
evaluator
string
required

The evaluator resource name to use for RLOR fine-tuning job.

​
name
string
​
displayName
string
​
createTime
string<date-time>
​
completedTime
string<date-time>

The completed time for the reinforcement fine-tuning job.

​
evaluationDataset
string

The name of a separate dataset to use for evaluation.

​
evalAutoCarveout
boolean

Whether to auto-carve the dataset for eval.

​
state
enum<string>
default:JOB_STATE_UNSPECIFIED

JobState represents the state an asynchronous job can be in.

  • JOB_STATE_PAUSED: Job is paused, typically due to account suspension or manual intervention.
Available options:
JOB_STATE_UNSPECIFIED,
JOB_STATE_CREATING,
JOB_STATE_RUNNING,
JOB_STATE_COMPLETED,
JOB_STATE_FAILED,
JOB_STATE_CANCELLED,
JOB_STATE_DELETING,
JOB_STATE_WRITING_RESULTS,
JOB_STATE_VALIDATING,
JOB_STATE_DELETING_CLEANING_UP,
JOB_STATE_PENDING,
JOB_STATE_EXPIRED,
JOB_STATE_RE_QUEUEING,
JOB_STATE_CREATING_INPUT_DATASET,
JOB_STATE_IDLE,
JOB_STATE_CANCELLING,
JOB_STATE_EARLY_STOPPED,
JOB_STATE_PAUSED
​
status
Mimics [https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto] · object
​
createdBy
string

The email address of the user who initiated this fine-tuning job.

​
trainingConfig
BaseTrainingConfig contains common configuration fields shared across different training job types. Next ID: 19 · object

Common training configurations.

​
wandbConfig
object

The Weights & Biases team/user account for logging training progress.

​
outputStats
string

The output dataset's aggregated stats for the evaluation job.

​
inferenceParameters
RFT inference parameters · object

RFT inference parameters.

​
chunkSize
integer<int32>

Data chunking for rollout, default size 200, enabled when dataset > 300. Valid range is 1-10,000.

​
outputMetrics
string
​
mcpServer
string
​
nodeCount
integer<int32>

The number of nodes to use for the fine-tuning job. If not specified, the default is 1.

​
lossConfig
object

Reinforcement learning loss method + hyperparameters for the underlying trainers.