List Models

Before creating a fine-tuning job, you need to check the list of models available, along with the customizable hyperparameters (hyper-parameters) that are supported for each model. You can use this information to select a model for fine-tuning and configure the appropriate hyperparameters according to their requirements.

Replace {{API_TOKEN}} with your actual token.

Example Request:

Curl

curl --location 'https://api.netmind.ai/v1/fine-tuning/template' \
--header 'Authorization: Bearer {{API_TOKEN}}'

Python

import requests

url = "https://api.netmind.ai/v1/fine-tuning/template"

payload = {}
headers = {
  'Authorization': 'Bearer {{API_TOKEN}}'
}

response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

Example Response:

[
    {
        "pricing": 0.01,
        "name": "llama-3-8b",
        "default_hyperparameters": {
            "per_device_train_batch_size": 1,
            "learning_rate": 0.0001,
            "num_train_epochs": 30
        }
    }
]