Create Sft Training Job

Create a new SFT (Supervised Fine-Tuning) training job.

POST /v1/preview/sft-training-jobs

Create Sft Training Job

cURL

curl --request POST \
  --url https://api.training.wandb.ai/v1/preview/sft-training-jobs \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "training_data_url": "<string>",
  "config": {
    "batch_size": 123,
    "learning_rate": 123,
    "metric_logging": {}
  },
  "experimental_config": {}
}
'

import requests

url = "https://api.training.wandb.ai/v1/preview/sft-training-jobs"

payload = {
    "model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "training_data_url": "<string>",
    "config": {
        "batch_size": 123,
        "learning_rate": 123,
        "metric_logging": {}
    },
    "experimental_config": {}
}
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({
    model_id: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
    training_data_url: '<string>',
    config: {batch_size: 123, learning_rate: 123, metric_logging: {}},
    experimental_config: {}
  })
};

fetch('https://api.training.wandb.ai/v1/preview/sft-training-jobs', 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.training.wandb.ai/v1/preview/sft-training-jobs",
  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([
    'model_id' => '3c90c3cc-0d44-4b50-8888-8dd25736052a',
    'training_data_url' => '<string>',
    'config' => [
        'batch_size' => 123,
        'learning_rate' => 123,
        'metric_logging' => [
                
        ]
    ],
    'experimental_config' => [
        
    ]
  ]),
  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.training.wandb.ai/v1/preview/sft-training-jobs"

	payload := strings.NewReader("{\n  \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"training_data_url\": \"<string>\",\n  \"config\": {\n    \"batch_size\": 123,\n    \"learning_rate\": 123,\n    \"metric_logging\": {}\n  },\n  \"experimental_config\": {}\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.training.wandb.ai/v1/preview/sft-training-jobs")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"training_data_url\": \"<string>\",\n  \"config\": {\n    \"batch_size\": 123,\n    \"learning_rate\": 123,\n    \"metric_logging\": {}\n  },\n  \"experimental_config\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.training.wandb.ai/v1/preview/sft-training-jobs")

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  \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"training_data_url\": \"<string>\",\n  \"config\": {\n    \"batch_size\": 123,\n    \"learning_rate\": 123,\n    \"metric_logging\": {}\n  },\n  \"experimental_config\": {}\n}"

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

200

{
  "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

Authorizations

Authorization · string · header · required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

Schema for creating a new SFT (Supervised Fine-Tuning) TrainingJob.

The client should upload the training data (trajectories.jsonl and metadata.json) to W&B Artifacts and provide the artifact URL.

model_id · string<uuid> · required

training_data_url · string · required

W&B artifact path for training data (e.g., 'wandb-artifact:///entity/project/artifact-name:version')

config · SFTTrainingConfig · object | null

Schema for SFT training config.

Show child attributes

experimental_config · Experimental Config · object | null

Response

200

application/json

Successful Response

Schema for TrainingJob response.

id · string<uuid> · required