Create finetune run
curl --request POST \
--url https://api.trymaitai.ai/api/v1/finetune-runs \
--header 'Content-Type: application/json' \
--header 'X-Maitai-Api-Key: <api-key>' \
--data '
{
"display_name": "Support SFT v1",
"base_model": "gpt-4o-mini-2024-07-18",
"status": "PENDING",
"meta": {
"training_method": "SFT",
"sub_dataset_id": 20,
"auto_test_set_ids": [
15
],
"evaluation_criteria_ids": [
3
]
}
}
'import requests
url = "https://api.trymaitai.ai/api/v1/finetune-runs"
payload = {
"display_name": "Support SFT v1",
"base_model": "gpt-4o-mini-2024-07-18",
"status": "PENDING",
"meta": {
"training_method": "SFT",
"sub_dataset_id": 20,
"auto_test_set_ids": [15],
"evaluation_criteria_ids": [3]
}
}
headers = {
"X-Maitai-Api-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-Maitai-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
display_name: 'Support SFT v1',
base_model: 'gpt-4o-mini-2024-07-18',
status: 'PENDING',
meta: {
training_method: 'SFT',
sub_dataset_id: 20,
auto_test_set_ids: [15],
evaluation_criteria_ids: [3]
}
})
};
fetch('https://api.trymaitai.ai/api/v1/finetune-runs', 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.trymaitai.ai/api/v1/finetune-runs",
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([
'display_name' => 'Support SFT v1',
'base_model' => 'gpt-4o-mini-2024-07-18',
'status' => 'PENDING',
'meta' => [
'training_method' => 'SFT',
'sub_dataset_id' => 20,
'auto_test_set_ids' => [
15
],
'evaluation_criteria_ids' => [
3
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-Maitai-Api-Key: <api-key>"
],
]);
$response = curl_exec($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.trymaitai.ai/api/v1/finetune-runs"
payload := strings.NewReader("{\n \"display_name\": \"Support SFT v1\",\n \"base_model\": \"gpt-4o-mini-2024-07-18\",\n \"status\": \"PENDING\",\n \"meta\": {\n \"training_method\": \"SFT\",\n \"sub_dataset_id\": 20,\n \"auto_test_set_ids\": [\n 15\n ],\n \"evaluation_criteria_ids\": [\n 3\n ]\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-Maitai-Api-Key", "<api-key>")
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.trymaitai.ai/api/v1/finetune-runs")
.header("X-Maitai-Api-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"display_name\": \"Support SFT v1\",\n \"base_model\": \"gpt-4o-mini-2024-07-18\",\n \"status\": \"PENDING\",\n \"meta\": {\n \"training_method\": \"SFT\",\n \"sub_dataset_id\": 20,\n \"auto_test_set_ids\": [\n 15\n ],\n \"evaluation_criteria_ids\": [\n 3\n ]\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.trymaitai.ai/api/v1/finetune-runs")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["X-Maitai-Api-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"display_name\": \"Support SFT v1\",\n \"base_model\": \"gpt-4o-mini-2024-07-18\",\n \"status\": \"PENDING\",\n \"meta\": {\n \"training_method\": \"SFT\",\n \"sub_dataset_id\": 20,\n \"auto_test_set_ids\": [\n 15\n ],\n \"evaluation_criteria_ids\": [\n 3\n ]\n }\n}"
response = http.request(request)
puts response.read_body{
"data": {
"id": 61,
"dataset_id": 20,
"base_model": "gpt-4o-mini-2024-07-18",
"status": "PENDING"
}
}Finetune Runs
Create finetune run
Start a new model finetuning job using a training dataset.
Pass ?force=true to bypass the pre-flight VRAM estimator; otherwise a config predicted to OOM returns 409 with a recommendation payload.
POST
/
finetune-runs
Create finetune run
curl --request POST \
--url https://api.trymaitai.ai/api/v1/finetune-runs \
--header 'Content-Type: application/json' \
--header 'X-Maitai-Api-Key: <api-key>' \
--data '
{
"display_name": "Support SFT v1",
"base_model": "gpt-4o-mini-2024-07-18",
"status": "PENDING",
"meta": {
"training_method": "SFT",
"sub_dataset_id": 20,
"auto_test_set_ids": [
15
],
"evaluation_criteria_ids": [
3
]
}
}
'import requests
url = "https://api.trymaitai.ai/api/v1/finetune-runs"
payload = {
"display_name": "Support SFT v1",
"base_model": "gpt-4o-mini-2024-07-18",
"status": "PENDING",
"meta": {
"training_method": "SFT",
"sub_dataset_id": 20,
"auto_test_set_ids": [15],
"evaluation_criteria_ids": [3]
}
}
headers = {
"X-Maitai-Api-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-Maitai-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
display_name: 'Support SFT v1',
base_model: 'gpt-4o-mini-2024-07-18',
status: 'PENDING',
meta: {
training_method: 'SFT',
sub_dataset_id: 20,
auto_test_set_ids: [15],
evaluation_criteria_ids: [3]
}
})
};
fetch('https://api.trymaitai.ai/api/v1/finetune-runs', 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.trymaitai.ai/api/v1/finetune-runs",
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([
'display_name' => 'Support SFT v1',
'base_model' => 'gpt-4o-mini-2024-07-18',
'status' => 'PENDING',
'meta' => [
'training_method' => 'SFT',
'sub_dataset_id' => 20,
'auto_test_set_ids' => [
15
],
'evaluation_criteria_ids' => [
3
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-Maitai-Api-Key: <api-key>"
],
]);
$response = curl_exec($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.trymaitai.ai/api/v1/finetune-runs"
payload := strings.NewReader("{\n \"display_name\": \"Support SFT v1\",\n \"base_model\": \"gpt-4o-mini-2024-07-18\",\n \"status\": \"PENDING\",\n \"meta\": {\n \"training_method\": \"SFT\",\n \"sub_dataset_id\": 20,\n \"auto_test_set_ids\": [\n 15\n ],\n \"evaluation_criteria_ids\": [\n 3\n ]\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-Maitai-Api-Key", "<api-key>")
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.trymaitai.ai/api/v1/finetune-runs")
.header("X-Maitai-Api-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"display_name\": \"Support SFT v1\",\n \"base_model\": \"gpt-4o-mini-2024-07-18\",\n \"status\": \"PENDING\",\n \"meta\": {\n \"training_method\": \"SFT\",\n \"sub_dataset_id\": 20,\n \"auto_test_set_ids\": [\n 15\n ],\n \"evaluation_criteria_ids\": [\n 3\n ]\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.trymaitai.ai/api/v1/finetune-runs")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["X-Maitai-Api-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"display_name\": \"Support SFT v1\",\n \"base_model\": \"gpt-4o-mini-2024-07-18\",\n \"status\": \"PENDING\",\n \"meta\": {\n \"training_method\": \"SFT\",\n \"sub_dataset_id\": 20,\n \"auto_test_set_ids\": [\n 15\n ],\n \"evaluation_criteria_ids\": [\n 3\n ]\n }\n}"
response = http.request(request)
puts response.read_body{
"data": {
"id": 61,
"dataset_id": 20,
"base_model": "gpt-4o-mini-2024-07-18",
"status": "PENDING"
}
}Authorizations
Your Maitai API key from the Portal.
Query Parameters
Body
application/json
Finetune training method and hyperparameters. To automatically run test sets after training completes, set meta.auto_test_set_ids (list of test-set IDs) and optionally meta.evaluation_criteria_ids (list of judge criteria IDs) — they run as soon as the run finishes.
Response
201 - application/json
Create finetune run.
A model finetuning job that trains a new model on a dataset.
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