Create Supervised Fine-tuning Job

curl --request POST \
  --url https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "dataset": "<string>",
  "displayName": "<string>",
  "awsS3Config": {
    "credentialsSecret": "<string>",
    "iamRoleArn": "<string>"
  },
  "azureBlobStorageConfig": {
    "credentialsSecret": "<string>",
    "managedIdentityClientId": "<string>",
    "tenantId": "<string>"
  },
  "outputModel": "<string>",
  "baseModel": "<string>",
  "warmStartFrom": "<string>",
  "jinjaTemplate": "<string>",
  "earlyStop": true,
  "epochs": 123,
  "learningRate": 123,
  "maxContextLength": 123,
  "loraRank": 123,
  "wandbConfig": {
    "enabled": true,
    "apiKey": "<string>",
    "project": "<string>",
    "entity": "<string>",
    "runId": "<string>"
  },
  "evaluationDataset": "<string>",
  "isTurbo": true,
  "evalAutoCarveout": true,
  "nodes": 123,
  "batchSize": 123,
  "mtpEnabled": true,
  "mtpNumDraftTokens": 123,
  "mtpFreezeBaseModel": true,
  "metricsFileSignedUrl": "<string>",
  "gradientAccumulationSteps": 123,
  "learningRateWarmupSteps": 123,
  "lrScheduler": {
    "constant": {},
    "linear": {
      "minLrRatio": 123,
      "decayRatio": 123
    },
    "cosine": {
      "minLrRatio": 123,
      "decayRatio": 123
    }
  },
  "batchSizeSamples": 123,
  "optimizerWeightDecay": 123,
  "purpose": "PURPOSE_UNSPECIFIED"
}
'

import requests

url = "https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs"

payload = {
    "dataset": "<string>",
    "displayName": "<string>",
    "awsS3Config": {
        "credentialsSecret": "<string>",
        "iamRoleArn": "<string>"
    },
    "azureBlobStorageConfig": {
        "credentialsSecret": "<string>",
        "managedIdentityClientId": "<string>",
        "tenantId": "<string>"
    },
    "outputModel": "<string>",
    "baseModel": "<string>",
    "warmStartFrom": "<string>",
    "jinjaTemplate": "<string>",
    "earlyStop": True,
    "epochs": 123,
    "learningRate": 123,
    "maxContextLength": 123,
    "loraRank": 123,
    "wandbConfig": {
        "enabled": True,
        "apiKey": "<string>",
        "project": "<string>",
        "entity": "<string>",
        "runId": "<string>"
    },
    "evaluationDataset": "<string>",
    "isTurbo": True,
    "evalAutoCarveout": True,
    "nodes": 123,
    "batchSize": 123,
    "mtpEnabled": True,
    "mtpNumDraftTokens": 123,
    "mtpFreezeBaseModel": True,
    "metricsFileSignedUrl": "<string>",
    "gradientAccumulationSteps": 123,
    "learningRateWarmupSteps": 123,
    "lrScheduler": {
        "constant": {},
        "linear": {
            "minLrRatio": 123,
            "decayRatio": 123
        },
        "cosine": {
            "minLrRatio": 123,
            "decayRatio": 123
        }
    },
    "batchSizeSamples": 123,
    "optimizerWeightDecay": 123,
    "purpose": "PURPOSE_UNSPECIFIED"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    dataset: '<string>',
    displayName: '<string>',
    awsS3Config: {credentialsSecret: '<string>', iamRoleArn: '<string>'},
    azureBlobStorageConfig: {
      credentialsSecret: '<string>',
      managedIdentityClientId: '<string>',
      tenantId: '<string>'
    },
    outputModel: '<string>',
    baseModel: '<string>',
    warmStartFrom: '<string>',
    jinjaTemplate: '<string>',
    earlyStop: true,
    epochs: 123,
    learningRate: 123,
    maxContextLength: 123,
    loraRank: 123,
    wandbConfig: {
      enabled: true,
      apiKey: '<string>',
      project: '<string>',
      entity: '<string>',
      runId: '<string>'
    },
    evaluationDataset: '<string>',
    isTurbo: true,
    evalAutoCarveout: true,
    nodes: 123,
    batchSize: 123,
    mtpEnabled: true,
    mtpNumDraftTokens: 123,
    mtpFreezeBaseModel: true,
    metricsFileSignedUrl: '<string>',
    gradientAccumulationSteps: 123,
    learningRateWarmupSteps: 123,
    lrScheduler: {
      constant: {},
      linear: {minLrRatio: 123, decayRatio: 123},
      cosine: {minLrRatio: 123, decayRatio: 123}
    },
    batchSizeSamples: 123,
    optimizerWeightDecay: 123,
    purpose: 'PURPOSE_UNSPECIFIED'
  })
};

fetch('https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'dataset' => '<string>',
    'displayName' => '<string>',
    'awsS3Config' => [
        'credentialsSecret' => '<string>',
        'iamRoleArn' => '<string>'
    ],
    'azureBlobStorageConfig' => [
        'credentialsSecret' => '<string>',
        'managedIdentityClientId' => '<string>',
        'tenantId' => '<string>'
    ],
    'outputModel' => '<string>',
    'baseModel' => '<string>',
    'warmStartFrom' => '<string>',
    'jinjaTemplate' => '<string>',
    'earlyStop' => true,
    'epochs' => 123,
    'learningRate' => 123,
    'maxContextLength' => 123,
    'loraRank' => 123,
    'wandbConfig' => [
        'enabled' => true,
        'apiKey' => '<string>',
        'project' => '<string>',
        'entity' => '<string>',
        'runId' => '<string>'
    ],
    'evaluationDataset' => '<string>',
    'isTurbo' => true,
    'evalAutoCarveout' => true,
    'nodes' => 123,
    'batchSize' => 123,
    'mtpEnabled' => true,
    'mtpNumDraftTokens' => 123,
    'mtpFreezeBaseModel' => true,
    'metricsFileSignedUrl' => '<string>',
    'gradientAccumulationSteps' => 123,
    'learningRateWarmupSteps' => 123,
    'lrScheduler' => [
        'constant' => [
                
        ],
        'linear' => [
                'minLrRatio' => 123,
                'decayRatio' => 123
        ],
        'cosine' => [
                'minLrRatio' => 123,
                'decayRatio' => 123
        ]
    ],
    'batchSizeSamples' => 123,
    'optimizerWeightDecay' => 123,
    'purpose' => 'PURPOSE_UNSPECIFIED'
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs"

	payload := strings.NewReader("{\n  \"dataset\": \"<string>\",\n  \"displayName\": \"<string>\",\n  \"awsS3Config\": {\n    \"credentialsSecret\": \"<string>\",\n    \"iamRoleArn\": \"<string>\"\n  },\n  \"azureBlobStorageConfig\": {\n    \"credentialsSecret\": \"<string>\",\n    \"managedIdentityClientId\": \"<string>\",\n    \"tenantId\": \"<string>\"\n  },\n  \"outputModel\": \"<string>\",\n  \"baseModel\": \"<string>\",\n  \"warmStartFrom\": \"<string>\",\n  \"jinjaTemplate\": \"<string>\",\n  \"earlyStop\": true,\n  \"epochs\": 123,\n  \"learningRate\": 123,\n  \"maxContextLength\": 123,\n  \"loraRank\": 123,\n  \"wandbConfig\": {\n    \"enabled\": true,\n    \"apiKey\": \"<string>\",\n    \"project\": \"<string>\",\n    \"entity\": \"<string>\",\n    \"runId\": \"<string>\"\n  },\n  \"evaluationDataset\": \"<string>\",\n  \"isTurbo\": true,\n  \"evalAutoCarveout\": true,\n  \"nodes\": 123,\n  \"batchSize\": 123,\n  \"mtpEnabled\": true,\n  \"mtpNumDraftTokens\": 123,\n  \"mtpFreezeBaseModel\": true,\n  \"metricsFileSignedUrl\": \"<string>\",\n  \"gradientAccumulationSteps\": 123,\n  \"learningRateWarmupSteps\": 123,\n  \"lrScheduler\": {\n    \"constant\": {},\n    \"linear\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    },\n    \"cosine\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    }\n  },\n  \"batchSizeSamples\": 123,\n  \"optimizerWeightDecay\": 123,\n  \"purpose\": \"PURPOSE_UNSPECIFIED\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"dataset\": \"<string>\",\n  \"displayName\": \"<string>\",\n  \"awsS3Config\": {\n    \"credentialsSecret\": \"<string>\",\n    \"iamRoleArn\": \"<string>\"\n  },\n  \"azureBlobStorageConfig\": {\n    \"credentialsSecret\": \"<string>\",\n    \"managedIdentityClientId\": \"<string>\",\n    \"tenantId\": \"<string>\"\n  },\n  \"outputModel\": \"<string>\",\n  \"baseModel\": \"<string>\",\n  \"warmStartFrom\": \"<string>\",\n  \"jinjaTemplate\": \"<string>\",\n  \"earlyStop\": true,\n  \"epochs\": 123,\n  \"learningRate\": 123,\n  \"maxContextLength\": 123,\n  \"loraRank\": 123,\n  \"wandbConfig\": {\n    \"enabled\": true,\n    \"apiKey\": \"<string>\",\n    \"project\": \"<string>\",\n    \"entity\": \"<string>\",\n    \"runId\": \"<string>\"\n  },\n  \"evaluationDataset\": \"<string>\",\n  \"isTurbo\": true,\n  \"evalAutoCarveout\": true,\n  \"nodes\": 123,\n  \"batchSize\": 123,\n  \"mtpEnabled\": true,\n  \"mtpNumDraftTokens\": 123,\n  \"mtpFreezeBaseModel\": true,\n  \"metricsFileSignedUrl\": \"<string>\",\n  \"gradientAccumulationSteps\": 123,\n  \"learningRateWarmupSteps\": 123,\n  \"lrScheduler\": {\n    \"constant\": {},\n    \"linear\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    },\n    \"cosine\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    }\n  },\n  \"batchSizeSamples\": 123,\n  \"optimizerWeightDecay\": 123,\n  \"purpose\": \"PURPOSE_UNSPECIFIED\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"dataset\": \"<string>\",\n  \"displayName\": \"<string>\",\n  \"awsS3Config\": {\n    \"credentialsSecret\": \"<string>\",\n    \"iamRoleArn\": \"<string>\"\n  },\n  \"azureBlobStorageConfig\": {\n    \"credentialsSecret\": \"<string>\",\n    \"managedIdentityClientId\": \"<string>\",\n    \"tenantId\": \"<string>\"\n  },\n  \"outputModel\": \"<string>\",\n  \"baseModel\": \"<string>\",\n  \"warmStartFrom\": \"<string>\",\n  \"jinjaTemplate\": \"<string>\",\n  \"earlyStop\": true,\n  \"epochs\": 123,\n  \"learningRate\": 123,\n  \"maxContextLength\": 123,\n  \"loraRank\": 123,\n  \"wandbConfig\": {\n    \"enabled\": true,\n    \"apiKey\": \"<string>\",\n    \"project\": \"<string>\",\n    \"entity\": \"<string>\",\n    \"runId\": \"<string>\"\n  },\n  \"evaluationDataset\": \"<string>\",\n  \"isTurbo\": true,\n  \"evalAutoCarveout\": true,\n  \"nodes\": 123,\n  \"batchSize\": 123,\n  \"mtpEnabled\": true,\n  \"mtpNumDraftTokens\": 123,\n  \"mtpFreezeBaseModel\": true,\n  \"metricsFileSignedUrl\": \"<string>\",\n  \"gradientAccumulationSteps\": 123,\n  \"learningRateWarmupSteps\": 123,\n  \"lrScheduler\": {\n    \"constant\": {},\n    \"linear\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    },\n    \"cosine\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    }\n  },\n  \"batchSizeSamples\": 123,\n  \"optimizerWeightDecay\": 123,\n  \"purpose\": \"PURPOSE_UNSPECIFIED\"\n}"

response = http.request(request)
puts response.read_body

{
  "dataset": "<string>",
  "name": "<string>",
  "displayName": "<string>",
  "createTime": "2023-11-07T05:31:56Z",
  "completedTime": "2023-11-07T05:31:56Z",
  "awsS3Config": {
    "credentialsSecret": "<string>",
    "iamRoleArn": "<string>"
  },
  "azureBlobStorageConfig": {
    "credentialsSecret": "<string>",
    "managedIdentityClientId": "<string>",
    "tenantId": "<string>"
  },
  "state": "JOB_STATE_UNSPECIFIED",
  "status": {
    "code": "OK",
    "message": "<string>"
  },
  "createdBy": "<string>",
  "outputModel": "<string>",
  "baseModel": "<string>",
  "warmStartFrom": "<string>",
  "jinjaTemplate": "<string>",
  "earlyStop": true,
  "epochs": 123,
  "learningRate": 123,
  "maxContextLength": 123,
  "loraRank": 123,
  "wandbConfig": {
    "enabled": true,
    "apiKey": "<string>",
    "project": "<string>",
    "entity": "<string>",
    "runId": "<string>",
    "url": "<string>"
  },
  "evaluationDataset": "<string>",
  "isTurbo": true,
  "evalAutoCarveout": true,
  "updateTime": "2023-11-07T05:31:56Z",
  "nodes": 123,
  "batchSize": 123,
  "mtpEnabled": true,
  "mtpNumDraftTokens": 123,
  "mtpFreezeBaseModel": true,
  "jobProgress": {
    "percent": 123,
    "epoch": 123,
    "totalInputRequests": 123,
    "totalProcessedRequests": 123,
    "successfullyProcessedRequests": 123,
    "failedRequests": 123,
    "outputRows": 123,
    "inputTokens": 123,
    "outputTokens": 123,
    "cachedInputTokenCount": 123
  },
  "metricsFileSignedUrl": "<string>",
  "trainerLogsSignedUrl": "<string>",
  "renderSamplesSignedUrl": "<string>",
  "gradientAccumulationSteps": 123,
  "learningRateWarmupSteps": 123,
  "lrScheduler": {
    "constant": {},
    "linear": {
      "minLrRatio": 123,
      "decayRatio": 123
    },
    "cosine": {
      "minLrRatio": 123,
      "decayRatio": 123
    }
  },
  "batchSizeSamples": 123,
  "estimatedCost": {
    "currencyCode": "<string>",
    "units": "<string>",
    "nanos": 123
  },
  "optimizerWeightDecay": 123,
  "purpose": "PURPOSE_UNSPECIFIED",
  "encryptionState": "ENCRYPTION_STATE_UNSPECIFIED"
}

POST

accounts

{account_id}

supervisedFineTuningJobs

Create Supervised Fine-tuning Job

curl --request POST \
  --url https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "dataset": "<string>",
  "displayName": "<string>",
  "awsS3Config": {
    "credentialsSecret": "<string>",
    "iamRoleArn": "<string>"
  },
  "azureBlobStorageConfig": {
    "credentialsSecret": "<string>",
    "managedIdentityClientId": "<string>",
    "tenantId": "<string>"
  },
  "outputModel": "<string>",
  "baseModel": "<string>",
  "warmStartFrom": "<string>",
  "jinjaTemplate": "<string>",
  "earlyStop": true,
  "epochs": 123,
  "learningRate": 123,
  "maxContextLength": 123,
  "loraRank": 123,
  "wandbConfig": {
    "enabled": true,
    "apiKey": "<string>",
    "project": "<string>",
    "entity": "<string>",
    "runId": "<string>"
  },
  "evaluationDataset": "<string>",
  "isTurbo": true,
  "evalAutoCarveout": true,
  "nodes": 123,
  "batchSize": 123,
  "mtpEnabled": true,
  "mtpNumDraftTokens": 123,
  "mtpFreezeBaseModel": true,
  "metricsFileSignedUrl": "<string>",
  "gradientAccumulationSteps": 123,
  "learningRateWarmupSteps": 123,
  "lrScheduler": {
    "constant": {},
    "linear": {
      "minLrRatio": 123,
      "decayRatio": 123
    },
    "cosine": {
      "minLrRatio": 123,
      "decayRatio": 123
    }
  },
  "batchSizeSamples": 123,
  "optimizerWeightDecay": 123,
  "purpose": "PURPOSE_UNSPECIFIED"
}
'

import requests

url = "https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs"

payload = {
    "dataset": "<string>",
    "displayName": "<string>",
    "awsS3Config": {
        "credentialsSecret": "<string>",
        "iamRoleArn": "<string>"
    },
    "azureBlobStorageConfig": {
        "credentialsSecret": "<string>",
        "managedIdentityClientId": "<string>",
        "tenantId": "<string>"
    },
    "outputModel": "<string>",
    "baseModel": "<string>",
    "warmStartFrom": "<string>",
    "jinjaTemplate": "<string>",
    "earlyStop": True,
    "epochs": 123,
    "learningRate": 123,
    "maxContextLength": 123,
    "loraRank": 123,
    "wandbConfig": {
        "enabled": True,
        "apiKey": "<string>",
        "project": "<string>",
        "entity": "<string>",
        "runId": "<string>"
    },
    "evaluationDataset": "<string>",
    "isTurbo": True,
    "evalAutoCarveout": True,
    "nodes": 123,
    "batchSize": 123,
    "mtpEnabled": True,
    "mtpNumDraftTokens": 123,
    "mtpFreezeBaseModel": True,
    "metricsFileSignedUrl": "<string>",
    "gradientAccumulationSteps": 123,
    "learningRateWarmupSteps": 123,
    "lrScheduler": {
        "constant": {},
        "linear": {
            "minLrRatio": 123,
            "decayRatio": 123
        },
        "cosine": {
            "minLrRatio": 123,
            "decayRatio": 123
        }
    },
    "batchSizeSamples": 123,
    "optimizerWeightDecay": 123,
    "purpose": "PURPOSE_UNSPECIFIED"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    dataset: '<string>',
    displayName: '<string>',
    awsS3Config: {credentialsSecret: '<string>', iamRoleArn: '<string>'},
    azureBlobStorageConfig: {
      credentialsSecret: '<string>',
      managedIdentityClientId: '<string>',
      tenantId: '<string>'
    },
    outputModel: '<string>',
    baseModel: '<string>',
    warmStartFrom: '<string>',
    jinjaTemplate: '<string>',
    earlyStop: true,
    epochs: 123,
    learningRate: 123,
    maxContextLength: 123,
    loraRank: 123,
    wandbConfig: {
      enabled: true,
      apiKey: '<string>',
      project: '<string>',
      entity: '<string>',
      runId: '<string>'
    },
    evaluationDataset: '<string>',
    isTurbo: true,
    evalAutoCarveout: true,
    nodes: 123,
    batchSize: 123,
    mtpEnabled: true,
    mtpNumDraftTokens: 123,
    mtpFreezeBaseModel: true,
    metricsFileSignedUrl: '<string>',
    gradientAccumulationSteps: 123,
    learningRateWarmupSteps: 123,
    lrScheduler: {
      constant: {},
      linear: {minLrRatio: 123, decayRatio: 123},
      cosine: {minLrRatio: 123, decayRatio: 123}
    },
    batchSizeSamples: 123,
    optimizerWeightDecay: 123,
    purpose: 'PURPOSE_UNSPECIFIED'
  })
};

fetch('https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'dataset' => '<string>',
    'displayName' => '<string>',
    'awsS3Config' => [
        'credentialsSecret' => '<string>',
        'iamRoleArn' => '<string>'
    ],
    'azureBlobStorageConfig' => [
        'credentialsSecret' => '<string>',
        'managedIdentityClientId' => '<string>',
        'tenantId' => '<string>'
    ],
    'outputModel' => '<string>',
    'baseModel' => '<string>',
    'warmStartFrom' => '<string>',
    'jinjaTemplate' => '<string>',
    'earlyStop' => true,
    'epochs' => 123,
    'learningRate' => 123,
    'maxContextLength' => 123,
    'loraRank' => 123,
    'wandbConfig' => [
        'enabled' => true,
        'apiKey' => '<string>',
        'project' => '<string>',
        'entity' => '<string>',
        'runId' => '<string>'
    ],
    'evaluationDataset' => '<string>',
    'isTurbo' => true,
    'evalAutoCarveout' => true,
    'nodes' => 123,
    'batchSize' => 123,
    'mtpEnabled' => true,
    'mtpNumDraftTokens' => 123,
    'mtpFreezeBaseModel' => true,
    'metricsFileSignedUrl' => '<string>',
    'gradientAccumulationSteps' => 123,
    'learningRateWarmupSteps' => 123,
    'lrScheduler' => [
        'constant' => [
                
        ],
        'linear' => [
                'minLrRatio' => 123,
                'decayRatio' => 123
        ],
        'cosine' => [
                'minLrRatio' => 123,
                'decayRatio' => 123
        ]
    ],
    'batchSizeSamples' => 123,
    'optimizerWeightDecay' => 123,
    'purpose' => 'PURPOSE_UNSPECIFIED'
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs"

	payload := strings.NewReader("{\n  \"dataset\": \"<string>\",\n  \"displayName\": \"<string>\",\n  \"awsS3Config\": {\n    \"credentialsSecret\": \"<string>\",\n    \"iamRoleArn\": \"<string>\"\n  },\n  \"azureBlobStorageConfig\": {\n    \"credentialsSecret\": \"<string>\",\n    \"managedIdentityClientId\": \"<string>\",\n    \"tenantId\": \"<string>\"\n  },\n  \"outputModel\": \"<string>\",\n  \"baseModel\": \"<string>\",\n  \"warmStartFrom\": \"<string>\",\n  \"jinjaTemplate\": \"<string>\",\n  \"earlyStop\": true,\n  \"epochs\": 123,\n  \"learningRate\": 123,\n  \"maxContextLength\": 123,\n  \"loraRank\": 123,\n  \"wandbConfig\": {\n    \"enabled\": true,\n    \"apiKey\": \"<string>\",\n    \"project\": \"<string>\",\n    \"entity\": \"<string>\",\n    \"runId\": \"<string>\"\n  },\n  \"evaluationDataset\": \"<string>\",\n  \"isTurbo\": true,\n  \"evalAutoCarveout\": true,\n  \"nodes\": 123,\n  \"batchSize\": 123,\n  \"mtpEnabled\": true,\n  \"mtpNumDraftTokens\": 123,\n  \"mtpFreezeBaseModel\": true,\n  \"metricsFileSignedUrl\": \"<string>\",\n  \"gradientAccumulationSteps\": 123,\n  \"learningRateWarmupSteps\": 123,\n  \"lrScheduler\": {\n    \"constant\": {},\n    \"linear\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    },\n    \"cosine\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    }\n  },\n  \"batchSizeSamples\": 123,\n  \"optimizerWeightDecay\": 123,\n  \"purpose\": \"PURPOSE_UNSPECIFIED\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"dataset\": \"<string>\",\n  \"displayName\": \"<string>\",\n  \"awsS3Config\": {\n    \"credentialsSecret\": \"<string>\",\n    \"iamRoleArn\": \"<string>\"\n  },\n  \"azureBlobStorageConfig\": {\n    \"credentialsSecret\": \"<string>\",\n    \"managedIdentityClientId\": \"<string>\",\n    \"tenantId\": \"<string>\"\n  },\n  \"outputModel\": \"<string>\",\n  \"baseModel\": \"<string>\",\n  \"warmStartFrom\": \"<string>\",\n  \"jinjaTemplate\": \"<string>\",\n  \"earlyStop\": true,\n  \"epochs\": 123,\n  \"learningRate\": 123,\n  \"maxContextLength\": 123,\n  \"loraRank\": 123,\n  \"wandbConfig\": {\n    \"enabled\": true,\n    \"apiKey\": \"<string>\",\n    \"project\": \"<string>\",\n    \"entity\": \"<string>\",\n    \"runId\": \"<string>\"\n  },\n  \"evaluationDataset\": \"<string>\",\n  \"isTurbo\": true,\n  \"evalAutoCarveout\": true,\n  \"nodes\": 123,\n  \"batchSize\": 123,\n  \"mtpEnabled\": true,\n  \"mtpNumDraftTokens\": 123,\n  \"mtpFreezeBaseModel\": true,\n  \"metricsFileSignedUrl\": \"<string>\",\n  \"gradientAccumulationSteps\": 123,\n  \"learningRateWarmupSteps\": 123,\n  \"lrScheduler\": {\n    \"constant\": {},\n    \"linear\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    },\n    \"cosine\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    }\n  },\n  \"batchSizeSamples\": 123,\n  \"optimizerWeightDecay\": 123,\n  \"purpose\": \"PURPOSE_UNSPECIFIED\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.fireworks.ai/v1/accounts/{account_id}/supervisedFineTuningJobs")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"dataset\": \"<string>\",\n  \"displayName\": \"<string>\",\n  \"awsS3Config\": {\n    \"credentialsSecret\": \"<string>\",\n    \"iamRoleArn\": \"<string>\"\n  },\n  \"azureBlobStorageConfig\": {\n    \"credentialsSecret\": \"<string>\",\n    \"managedIdentityClientId\": \"<string>\",\n    \"tenantId\": \"<string>\"\n  },\n  \"outputModel\": \"<string>\",\n  \"baseModel\": \"<string>\",\n  \"warmStartFrom\": \"<string>\",\n  \"jinjaTemplate\": \"<string>\",\n  \"earlyStop\": true,\n  \"epochs\": 123,\n  \"learningRate\": 123,\n  \"maxContextLength\": 123,\n  \"loraRank\": 123,\n  \"wandbConfig\": {\n    \"enabled\": true,\n    \"apiKey\": \"<string>\",\n    \"project\": \"<string>\",\n    \"entity\": \"<string>\",\n    \"runId\": \"<string>\"\n  },\n  \"evaluationDataset\": \"<string>\",\n  \"isTurbo\": true,\n  \"evalAutoCarveout\": true,\n  \"nodes\": 123,\n  \"batchSize\": 123,\n  \"mtpEnabled\": true,\n  \"mtpNumDraftTokens\": 123,\n  \"mtpFreezeBaseModel\": true,\n  \"metricsFileSignedUrl\": \"<string>\",\n  \"gradientAccumulationSteps\": 123,\n  \"learningRateWarmupSteps\": 123,\n  \"lrScheduler\": {\n    \"constant\": {},\n    \"linear\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    },\n    \"cosine\": {\n      \"minLrRatio\": 123,\n      \"decayRatio\": 123\n    }\n  },\n  \"batchSizeSamples\": 123,\n  \"optimizerWeightDecay\": 123,\n  \"purpose\": \"PURPOSE_UNSPECIFIED\"\n}"

response = http.request(request)
puts response.read_body

{
  "dataset": "<string>",
  "name": "<string>",
  "displayName": "<string>",
  "createTime": "2023-11-07T05:31:56Z",
  "completedTime": "2023-11-07T05:31:56Z",
  "awsS3Config": {
    "credentialsSecret": "<string>",
    "iamRoleArn": "<string>"
  },
  "azureBlobStorageConfig": {
    "credentialsSecret": "<string>",
    "managedIdentityClientId": "<string>",
    "tenantId": "<string>"
  },
  "state": "JOB_STATE_UNSPECIFIED",
  "status": {
    "code": "OK",
    "message": "<string>"
  },
  "createdBy": "<string>",
  "outputModel": "<string>",
  "baseModel": "<string>",
  "warmStartFrom": "<string>",
  "jinjaTemplate": "<string>",
  "earlyStop": true,
  "epochs": 123,
  "learningRate": 123,
  "maxContextLength": 123,
  "loraRank": 123,
  "wandbConfig": {
    "enabled": true,
    "apiKey": "<string>",
    "project": "<string>",
    "entity": "<string>",
    "runId": "<string>",
    "url": "<string>"
  },
  "evaluationDataset": "<string>",
  "isTurbo": true,
  "evalAutoCarveout": true,
  "updateTime": "2023-11-07T05:31:56Z",
  "nodes": 123,
  "batchSize": 123,
  "mtpEnabled": true,
  "mtpNumDraftTokens": 123,
  "mtpFreezeBaseModel": true,
  "jobProgress": {
    "percent": 123,
    "epoch": 123,
    "totalInputRequests": 123,
    "totalProcessedRequests": 123,
    "successfullyProcessedRequests": 123,
    "failedRequests": 123,
    "outputRows": 123,
    "inputTokens": 123,
    "outputTokens": 123,
    "cachedInputTokenCount": 123
  },
  "metricsFileSignedUrl": "<string>",
  "trainerLogsSignedUrl": "<string>",
  "renderSamplesSignedUrl": "<string>",
  "gradientAccumulationSteps": 123,
  "learningRateWarmupSteps": 123,
  "lrScheduler": {
    "constant": {},
    "linear": {
      "minLrRatio": 123,
      "decayRatio": 123
    },
    "cosine": {
      "minLrRatio": 123,
      "decayRatio": 123
    }
  },
  "batchSizeSamples": 123,
  "estimatedCost": {
    "currencyCode": "<string>",
    "units": "<string>",
    "nanos": 123
  },
  "optimizerWeightDecay": 123,
  "purpose": "PURPOSE_UNSPECIFIED",
  "encryptionState": "ENCRYPTION_STATE_UNSPECIFIED"
}

Learning rate scheduler

Supervised fine-tuning jobs accept an optional lrScheduler object on the request body. Set exactly one of constant, linear, or cosine. When omitted, the trainer uses a constant learning rate after warmup. Configure warmup separately with learningRateWarmupSteps (not inside lrScheduler).

Schedule	Object shape	Notes
`constant`	`{ "constant": {} }`	Flat LR after warmup
`linear`	`{ "linear": { "minLrRatio": 0.1, "decayRatio": 0.8 } }`	Linear decay toward `learningRate * minLrRatio`
`cosine`	`{ "cosine": { "minLrRatio": 0.1, "decayRatio": 0.8 } }`	Cosine annealing toward `learningRate * minLrRatio`

For linear and cosine:

minLrRatio — floor LR as a fraction of learningRate (0.0–1.0).
decayRatio — fraction of total training steps over which to decay. Omit or set 0 to decay over the full run.

linear and cosine require a base model on the Training V2 path. V1-routed models only support a constant schedule.

Example: cosine schedule

{
  "supervisedFineTuningJob": {
    "baseModel": "accounts/my-account/models/qwen3-8b",
    "dataset": "accounts/my-account/datasets/my-data",
    "outputModel": "accounts/my-account/models/my-tuned-model",
    "learningRate": 0.0001,
    "learningRateWarmupSteps": 10,
    "lrScheduler": {
      "cosine": {
        "minLrRatio": 0.1,
        "decayRatio": 0.8
      }
    }
  }
}

See also Fine-tuning models for CLI equivalents (--learning-rate-scheduler, --learning-rate-min-lr-ratio, --learning-rate-decay-ratio).

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

Query Parameters

supervisedFineTuningJobId

string

ID of the supervised fine-tuning job, a random UUID will be generated if not specified.

Body

application/json

dataset

string

required

The name of the dataset used for training.

displayName

string

awsS3Config

object

The AWS configuration for S3 dataset access.

Show child attributes

azureBlobStorageConfig

object

The Azure configuration for Azure Blob Storage dataset access.

Show child attributes

outputModel

string

The model ID to be assigned to the resulting fine-tuned model. If not specified, the job ID will be used.

baseModel

string

The name of the base model to be fine-tuned Only one of 'base_model' or 'warm_start_from' should be specified.

warmStartFrom

string

The PEFT addon model in Fireworks format to be fine-tuned from Only one of 'base_model' or 'warm_start_from' should be specified.

jinjaTemplate

string

Deprecated: literal Jinja templates are not supported by Training V2. Conversation rendering is selected from the base model's registered renderer configuration instead.

earlyStop

boolean

Deprecated: early stopping is not supported by managed training.

epochs

integer<int32>

The number of epochs to train for.

learningRate

number<float>

The learning rate used for training.

maxContextLength

integer<int32>

The maximum context length to use with the model.

loraRank

integer<int32>

The rank of the LoRA layers.

wandbConfig

object

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

Show child attributes

evaluationDataset

string

The name of a separate dataset to use for evaluation.

isTurbo

boolean

Whether to run the fine-tuning job in turbo mode.

evalAutoCarveout

boolean

Whether to auto-carve the dataset for eval.

nodes

integer<int32>

Deprecated: multi-node scheduling is now handled by the cookbook orchestrator in V2 workflows. This field is ignored for V2 jobs and will be removed in a future release.

batchSize

integer<int32>

Deprecated: legacy V1 token budget. Training V2 batches by samples via batch_size_samples.

mtpEnabled

boolean

Deprecated: MTP is no longer supported by managed training. This field is retained for API wire compatibility only.

mtpNumDraftTokens

integer<int32>

Deprecated: see mtp_enabled.

mtpFreezeBaseModel

boolean

Deprecated: see mtp_enabled.

metricsFileSignedUrl

string

gradientAccumulationSteps

integer<int32>

Deprecated: legacy V1 gradient accumulation. Training V2 batches by samples via batch_size_samples and rejects this field when set.

learningRateWarmupSteps

integer<int32>

lrScheduler

object

The learning-rate schedule (constant/linear/cosine + per-type knobs). When unset, the trainer uses the legacy constant schedule.

Show child attributes

batchSizeSamples

integer<int32>

The number of samples per gradient batch.

optimizerWeightDecay

number<float>

Weight decay (L2 regularization) for optimizer.

purpose

enum<string>

default:PURPOSE_UNSPECIFIED

Scheduling purpose for this job.

Available options:

PURPOSE_UNSPECIFIED,

PURPOSE_PILOT

Response

200 - application/json

A successful response.

dataset

string

required

The name of the dataset used for training.

name

string

read-only

displayName

string

createTime

string<date-time>

read-only

completedTime

string<date-time>

read-only

awsS3Config

object

The AWS configuration for S3 dataset access.

Show child attributes

azureBlobStorageConfig

object

The Azure configuration for Azure Blob Storage dataset access.

Show child attributes

state

enum<string>

default:JOB_STATE_UNSPECIFIED

read-only

JobState represents the state an asynchronous job can be in.

JOB_STATE_PAUSED: Job is paused, typically due to account suspension or manual intervention.
JOB_STATE_DELETED: Job has been deleted.
JOB_STATE_ARCHIVED: User-facing state for jobs whose row is retained post-delete (e.g. RLOR trainers within the checkpoint retention window). The internal row is still in JOB_STATE_DELETED; the gateway translates it to ARCHIVED on public responses.

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,

JOB_STATE_DELETED,

JOB_STATE_ARCHIVED

status

Mimics [https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto] · object

read-only

Show child attributes

createdBy

string

read-only

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

outputModel

string

The model ID to be assigned to the resulting fine-tuned model. If not specified, the job ID will be used.

baseModel

string

The name of the base model to be fine-tuned Only one of 'base_model' or 'warm_start_from' should be specified.

warmStartFrom

string

The PEFT addon model in Fireworks format to be fine-tuned from Only one of 'base_model' or 'warm_start_from' should be specified.

jinjaTemplate

string

Deprecated: literal Jinja templates are not supported by Training V2. Conversation rendering is selected from the base model's registered renderer configuration instead.

earlyStop

boolean

Deprecated: early stopping is not supported by managed training.

epochs

integer<int32>

The number of epochs to train for.

learningRate

number<float>

The learning rate used for training.

maxContextLength

integer<int32>

The maximum context length to use with the model.

loraRank

integer<int32>

The rank of the LoRA layers.

wandbConfig

object

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

Show child attributes

evaluationDataset

string

The name of a separate dataset to use for evaluation.

isTurbo

boolean

Whether to run the fine-tuning job in turbo mode.

evalAutoCarveout

boolean

Whether to auto-carve the dataset for eval.

updateTime

string<date-time>

read-only

The update time for the supervised fine-tuning job.

nodes

integer<int32>

Deprecated: multi-node scheduling is now handled by the cookbook orchestrator in V2 workflows. This field is ignored for V2 jobs and will be removed in a future release.

batchSize

integer<int32>

Deprecated: legacy V1 token budget. Training V2 batches by samples via batch_size_samples.

mtpEnabled

boolean

Deprecated: MTP is no longer supported by managed training. This field is retained for API wire compatibility only.

mtpNumDraftTokens

integer<int32>

Deprecated: see mtp_enabled.

mtpFreezeBaseModel

boolean

Deprecated: see mtp_enabled.

jobProgress

object

read-only

Job progress.

Show child attributes

metricsFileSignedUrl

string

trainerLogsSignedUrl

string

read-only

The signed URL for the trainer logs file (stdout/stderr). Only populated if the account has trainer log reading enabled.

renderSamplesSignedUrl

string

read-only

The signed URL for orchestrator-rendered token IDs and loss masks.

gradientAccumulationSteps

integer<int32>

Deprecated: legacy V1 gradient accumulation. Training V2 batches by samples via batch_size_samples and rejects this field when set.

learningRateWarmupSteps

integer<int32>

lrScheduler

object

The learning-rate schedule (constant/linear/cosine + per-type knobs). When unset, the trainer uses the legacy constant schedule.

Show child attributes

batchSizeSamples

integer<int32>

The number of samples per gradient batch.

estimatedCost

object

read-only

The estimated cost of the job.

Show child attributes

optimizerWeightDecay

number<float>

Weight decay (L2 regularization) for optimizer.

purpose

enum<string>

default:PURPOSE_UNSPECIFIED

Scheduling purpose for this job.

Available options:

PURPOSE_UNSPECIFIED,

PURPOSE_PILOT

encryptionState

enum<string>

default:ENCRYPTION_STATE_UNSPECIFIED

read-only

CMEK encryption state (authoritative, stamped at creation).

Available options:

ENCRYPTION_STATE_UNSPECIFIED,

ENCRYPTION_STATE_PLAINTEXT,

ENCRYPTION_STATE_CMEK

Validate Dataset Upload

List Supervised Fine-tuning Jobs

⌘I

API Reference

Inference

Deployments

Fine-tuning

Evals

Multimedia

Admin

Create Supervised Fine-tuning Job

Learning rate scheduler

Example: cosine schedule

Authorizations

Path Parameters

Query Parameters

Body

Response

​Learning rate scheduler

​Example: cosine schedule

Authorizations

Path Parameters

Query Parameters

Body

Response

Learning rate scheduler

Example: cosine schedule