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

# Models

> Choose gateway models for evaluations and manage team-owned trainable model forks on HUD.

The **model catalog** is the set of models available through the HUD inference gateway. An
evaluation selects models by their catalog API name, so swapping the model changes nothing about
the taskset or environment.

Open [Models](https://hud.ai/models) to search the catalog, filter to trainable or private models,
and copy the API name used by the SDK.

```python theme={"dark"}
from hud.agents import create_agent

agent = create_agent("model-api-name")
```

## Public and team models

| Model               | What it is                                                                                  |
| ------------------- | ------------------------------------------------------------------------------------------- |
| **Public model**    | A shared gateway entry backed by a supported provider.                                      |
| **Team model**      | A private model owned by your active team.                                                  |
| **Trainable model** | A model that can be forked and updated through the HUD training API.                        |
| **Checkpoint**      | A saved set of weights for a trainable model. One checkpoint is promoted for new inference. |

Models shown in the evaluation dialog have an available checkpoint. A single evaluation can select
several models and run each one against the same taskset.

## Read a model page

| Tab             | What it shows                                                      |
| --------------- | ------------------------------------------------------------------ |
| **Overview**    | Provider, visibility, API name, routes, price, and context window. |
| **Results**     | Evaluation results associated with the model.                      |
| **Checkpoints** | Saved checkpoints for a trainable model.                           |
| **Logs**        | Training and model operation logs.                                 |
| **Settings**    | Editable metadata and controls for a team-owned model.             |

The exact provider route is an implementation detail of the catalog entry. Use the model's API name
in the SDK and let the gateway resolve the active route.

## Create a trainable model

Select **New model**, choose a trainable base model, and give the fork a name. The new team-owned
model inherits the source model's base weights or its currently promoted checkpoint, then starts
its own checkpoint history.

Creating the model does not train it. Use the
[`TrainingClient`](/v6/reference/training#trainingclient) to collect rollouts, apply updates, and
promote checkpoints behind the same model API name.

## Credentials and cost

A `HUD_API_KEY` authenticates gateway requests and attributes usage to your active team. Provider
credentials configured under **BYOK** can route eligible requests through your own provider
account. See [team settings](/platform/team) for keys, usage, and limits.

With a model selected, run it against a fixed suite in
[evaluations](/platform/evaluations).