> ## Documentation Index
> Fetch the complete documentation index at: https://docs.scitix.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Register and manage custom models

> Register training artifacts or your own model files as Custom Models in Siverse, and manage registered models.

Custom models are used to register training artifacts, self-developed models, or external model files in Models. When registering a model, Specify its ownership, Version, storage location, tags, Description, and metadata. After registration, the model can be referenced in Develop, Training, or Inference.

## Pre-Operation Confirmation

* The model files have been saved to storage accessible from the target region and cluster.
* The current account has permission to access the storage and path.
* The model name, version, and path have been confirmed to avoid difficulty distinguishing between model assets after registration.

## Register a Custom Model

1. Go to **Models > Custom**.

2. Click **Create**.

3. Enter the model basic information.

   | Parameter | Description |
   | - | - |
   | Region and cluster | Determines the resource scope to which the model is registered and affects the scope of workloads that can subsequently reference the model. |
   | Name | A custom model name used to identify the model in the model list and when referencing the model. |
   | Type | The model task type. |
   | Version | The model version number, used to distinguish different iterations of the same model. |
   | Tags | Used to search for and categorize models. |
   | Description | Describes the model's source, purpose, training data, or usage considerations. |

4. Configure the model storage location.

   | Parameter | Description |
   | - | - |
   | Storage | Select the storage type where the model files are located. Currently, **volume** can be selected in the creation form. |
   | Storage | Select the storage resource for saving the model files. |
   | Dir | Enter the relative path of the model files in the storage. |

   The Dir must be a relative path. Before registration, ensure that this directory contains the weights, configuration, tokenizer, and other files required to load the model later.

5. Enter metadata.

   | Parameter | Description |
   | - | - |
   | Architecture | Select or enter the model architecture to describe the model structure or series. |
   | Data Type | Select the data type used for model weights or inference. |
   | Parameters | Enter the number of model parameters, in B. |
   | Metrics | Add model evaluation metrics. |

6. Click **Create**.

After submission, return to the custom model list. Search for models by region, cluster, or name, and verify that the model can be found and used in subsequent references.

## Manage Custom Models

In **Models > Custom**, you can query custom models by region, cluster, and name. When managing models, pay particular attention to the model version, storage location, tags, and deployment status.

To delete custom model records that are no longer needed, select one or more models and then delete them. Before deleting, ensure that the model is not depended on by any inference services, development environments, or training tasks that still need to be retained. The **Deployed** information in the list can be used to determine whether the model is in use.


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