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POST
/
api
/
llm
/
train
/
Start a new LLM/VLM training job
curl --request POST \
  --url https://training-suite.uat-infer.shakticloud.ai/api/llm/train/ \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: multipart/form-data' \
  --form 'training_config_json={
  "model_type": "vlm",
  "base_model": "AIDC-AI/Ovis1.6-Llama3.2-3B",
  "adapter": {
    "lora_r": 16
  },
  "quantization": {
    "bits": 4,
    "llm_int8_threshold": 6,
    "llm_int8_has_fp16_weight": false,
    "bnb_4bit_compute_dtype": "float16",
    "bnb_4bit_use_double_quant": true,
    "bnb_4bit_quant_type": "nf4"
  },
  "trainer": {
    "type": "finetune",
    "learning_rate": 0.0001,
    "batch_size": 1,
    "epochs": 1,
    "gradient_accumulation_steps": 16,
    "logging_steps": 1
  },
  "image_column": "url",
  "output_column": "caption",
  "prompt": {
    "template": "Could you please interpret the image and write a detailed caption?"
  }
}
' \
  --form experiment_name=my_first_vlm_job \
  --form csv_file='@example-file' \
  --form org=0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d
import requests

url = "https://training-suite.uat-infer.shakticloud.ai/api/llm/train/"

files = { "csv_file": ("example-file", open("example-file", "rb")) }
payload = {
    "training_config_json": "{
  \"model_type\": \"vlm\",
  \"base_model\": \"AIDC-AI/Ovis1.6-Llama3.2-3B\",
  \"adapter\": {
    \"lora_r\": 16
  },
  \"quantization\": {
    \"bits\": 4,
    \"llm_int8_threshold\": 6,
    \"llm_int8_has_fp16_weight\": false,
    \"bnb_4bit_compute_dtype\": \"float16\",
    \"bnb_4bit_use_double_quant\": true,
    \"bnb_4bit_quant_type\": \"nf4\"
  },
  \"trainer\": {
    \"type\": \"finetune\",
    \"learning_rate\": 0.0001,
    \"batch_size\": 1,
    \"epochs\": 1,
    \"gradient_accumulation_steps\": 16,
    \"logging_steps\": 1
  },
  \"image_column\": \"url\",
  \"output_column\": \"caption\",
  \"prompt\": {
    \"template\": \"Could you please interpret the image and write a detailed caption?\"
  }
}
",
    "experiment_name": "my_first_vlm_job",
    "org": "0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d"
}
headers = {"Authorization": "Bearer <token>"}

response = requests.post(url, data=payload, files=files, headers=headers)

print(response.text)
const form = new FormData();
form.append('training_config_json', '{
  "model_type": "vlm",
  "base_model": "AIDC-AI/Ovis1.6-Llama3.2-3B",
  "adapter": {
    "lora_r": 16
  },
  "quantization": {
    "bits": 4,
    "llm_int8_threshold": 6,
    "llm_int8_has_fp16_weight": false,
    "bnb_4bit_compute_dtype": "float16",
    "bnb_4bit_use_double_quant": true,
    "bnb_4bit_quant_type": "nf4"
  },
  "trainer": {
    "type": "finetune",
    "learning_rate": 0.0001,
    "batch_size": 1,
    "epochs": 1,
    "gradient_accumulation_steps": 16,
    "logging_steps": 1
  },
  "image_column": "url",
  "output_column": "caption",
  "prompt": {
    "template": "Could you please interpret the image and write a detailed caption?"
  }
}
');
form.append('experiment_name', 'my_first_vlm_job');
form.append('csv_file', '<string>');
form.append('org', '0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d');

const options = {method: 'POST', headers: {Authorization: 'Bearer <token>'}};

options.body = form;

fetch('https://training-suite.uat-infer.shakticloud.ai/api/llm/train/', 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://training-suite.uat-infer.shakticloud.ai/api/llm/train/",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => "-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"training_config_json\"\r\n\r\n{\r\n  \"model_type\": \"vlm\",\r\n  \"base_model\": \"AIDC-AI/Ovis1.6-Llama3.2-3B\",\r\n  \"adapter\": {\r\n    \"lora_r\": 16\r\n  },\r\n  \"quantization\": {\r\n    \"bits\": 4,\r\n    \"llm_int8_threshold\": 6,\r\n    \"llm_int8_has_fp16_weight\": false,\r\n    \"bnb_4bit_compute_dtype\": \"float16\",\r\n    \"bnb_4bit_use_double_quant\": true,\r\n    \"bnb_4bit_quant_type\": \"nf4\"\r\n  },\r\n  \"trainer\": {\r\n    \"type\": \"finetune\",\r\n    \"learning_rate\": 0.0001,\r\n    \"batch_size\": 1,\r\n    \"epochs\": 1,\r\n    \"gradient_accumulation_steps\": 16,\r\n    \"logging_steps\": 1\r\n  },\r\n  \"image_column\": \"url\",\r\n  \"output_column\": \"caption\",\r\n  \"prompt\": {\r\n    \"template\": \"Could you please interpret the image and write a detailed caption?\"\r\n  }\r\n}\r\n\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"experiment_name\"\r\n\r\nmy_first_vlm_job\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"csv_file\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"org\"\r\n\r\n0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d\r\n-----011000010111000001101001--",
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: multipart/form-data"
  ],
]);

$response = curl_exec($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://training-suite.uat-infer.shakticloud.ai/api/llm/train/"

	payload := strings.NewReader("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"training_config_json\"\r\n\r\n{\r\n  \"model_type\": \"vlm\",\r\n  \"base_model\": \"AIDC-AI/Ovis1.6-Llama3.2-3B\",\r\n  \"adapter\": {\r\n    \"lora_r\": 16\r\n  },\r\n  \"quantization\": {\r\n    \"bits\": 4,\r\n    \"llm_int8_threshold\": 6,\r\n    \"llm_int8_has_fp16_weight\": false,\r\n    \"bnb_4bit_compute_dtype\": \"float16\",\r\n    \"bnb_4bit_use_double_quant\": true,\r\n    \"bnb_4bit_quant_type\": \"nf4\"\r\n  },\r\n  \"trainer\": {\r\n    \"type\": \"finetune\",\r\n    \"learning_rate\": 0.0001,\r\n    \"batch_size\": 1,\r\n    \"epochs\": 1,\r\n    \"gradient_accumulation_steps\": 16,\r\n    \"logging_steps\": 1\r\n  },\r\n  \"image_column\": \"url\",\r\n  \"output_column\": \"caption\",\r\n  \"prompt\": {\r\n    \"template\": \"Could you please interpret the image and write a detailed caption?\"\r\n  }\r\n}\r\n\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"experiment_name\"\r\n\r\nmy_first_vlm_job\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"csv_file\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"org\"\r\n\r\n0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d\r\n-----011000010111000001101001--")

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

	req.Header.Add("Authorization", "Bearer <token>")

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

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

	fmt.Println(string(body))

}
HttpResponse<String> response = Unirest.post("https://training-suite.uat-infer.shakticloud.ai/api/llm/train/")
  .header("Authorization", "Bearer <token>")
  .body("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"training_config_json\"\r\n\r\n{\r\n  \"model_type\": \"vlm\",\r\n  \"base_model\": \"AIDC-AI/Ovis1.6-Llama3.2-3B\",\r\n  \"adapter\": {\r\n    \"lora_r\": 16\r\n  },\r\n  \"quantization\": {\r\n    \"bits\": 4,\r\n    \"llm_int8_threshold\": 6,\r\n    \"llm_int8_has_fp16_weight\": false,\r\n    \"bnb_4bit_compute_dtype\": \"float16\",\r\n    \"bnb_4bit_use_double_quant\": true,\r\n    \"bnb_4bit_quant_type\": \"nf4\"\r\n  },\r\n  \"trainer\": {\r\n    \"type\": \"finetune\",\r\n    \"learning_rate\": 0.0001,\r\n    \"batch_size\": 1,\r\n    \"epochs\": 1,\r\n    \"gradient_accumulation_steps\": 16,\r\n    \"logging_steps\": 1\r\n  },\r\n  \"image_column\": \"url\",\r\n  \"output_column\": \"caption\",\r\n  \"prompt\": {\r\n    \"template\": \"Could you please interpret the image and write a detailed caption?\"\r\n  }\r\n}\r\n\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"experiment_name\"\r\n\r\nmy_first_vlm_job\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"csv_file\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"org\"\r\n\r\n0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d\r\n-----011000010111000001101001--")
  .asString();
require 'uri'
require 'net/http'

url = URI("https://training-suite.uat-infer.shakticloud.ai/api/llm/train/")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request.body = "-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"training_config_json\"\r\n\r\n{\r\n  \"model_type\": \"vlm\",\r\n  \"base_model\": \"AIDC-AI/Ovis1.6-Llama3.2-3B\",\r\n  \"adapter\": {\r\n    \"lora_r\": 16\r\n  },\r\n  \"quantization\": {\r\n    \"bits\": 4,\r\n    \"llm_int8_threshold\": 6,\r\n    \"llm_int8_has_fp16_weight\": false,\r\n    \"bnb_4bit_compute_dtype\": \"float16\",\r\n    \"bnb_4bit_use_double_quant\": true,\r\n    \"bnb_4bit_quant_type\": \"nf4\"\r\n  },\r\n  \"trainer\": {\r\n    \"type\": \"finetune\",\r\n    \"learning_rate\": 0.0001,\r\n    \"batch_size\": 1,\r\n    \"epochs\": 1,\r\n    \"gradient_accumulation_steps\": 16,\r\n    \"logging_steps\": 1\r\n  },\r\n  \"image_column\": \"url\",\r\n  \"output_column\": \"caption\",\r\n  \"prompt\": {\r\n    \"template\": \"Could you please interpret the image and write a detailed caption?\"\r\n  }\r\n}\r\n\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"experiment_name\"\r\n\r\nmy_first_vlm_job\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"csv_file\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"org\"\r\n\r\n0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d\r\n-----011000010111000001101001--"

response = http.request(request)
puts response.read_body
{
  "request_id": "<string>",
  "status": "<string>",
  "message": "<string>"
}

Authorizations

​
Authorization
string
header
required

JWT token for authentication

Headers

​
Authorization
string
required

Bearer token for authentication and authorization.

Body

multipart/form-data
​
training_config_json
string
required

JSON-formatted string containing the model training configuration.

Example:

"{\n \"model_type\": \"vlm\",\n \"base_model\": \"AIDC-AI/Ovis1.6-Llama3.2-3B\",\n \"adapter\": {\n \"lora_r\": 16\n },\n \"quantization\": {\n \"bits\": 4,\n \"llm_int8_threshold\": 6,\n \"llm_int8_has_fp16_weight\": false,\n \"bnb_4bit_compute_dtype\": \"float16\",\n \"bnb_4bit_use_double_quant\": true,\n \"bnb_4bit_quant_type\": \"nf4\"\n },\n \"trainer\": {\n \"type\": \"finetune\",\n \"learning_rate\": 0.0001,\n \"batch_size\": 1,\n \"epochs\": 1,\n \"gradient_accumulation_steps\": 16,\n \"logging_steps\": 1\n },\n \"image_column\": \"url\",\n \"output_column\": \"caption\",\n \"prompt\": {\n \"template\": \"Could you please interpret the image and write a detailed caption?\"\n }\n}\n"

​
experiment_name
string
required

Name assigned to the training experiment.

Example:

"my_first_vlm_job"

​
csv_file
file
required

CSV or JSONL file containing the training dataset.

​
org
string
required

Organization ID associated with the training job.

Example:

"0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4d"

Response

200 - application/json

Training job submitted successfully.

​
request_id
string

Unique identifier for the training job request

​
status
string

Initial status of the training job

​
message
string

Additional information about the job submission