> ## 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.

# Training

> Run training from the HUD SDK, then inspect model checkpoints, logs, and evaluation results on the platform.

You create the model on the platform, run the training loop from Python, then come back to the
model page to see what changed.

## Create a trainable model

Open [Models](https://hud.ai/models), select **New model**, and choose a trainable base. HUD creates
a private model for your team with its own API name and checkpoint history.

The same operation is available from the CLI:

```bash theme={"dark"}
hud models fork Qwen/Qwen3.5-4B --name arith-rl
```

## Train from Python

Run a [taskset](/platform/tasksets) in groups and pass the graded rollouts to `TrainingClient`.
Each `trainer.step(...)` applies the update, saves a checkpoint, and promotes it for the next
rollout.

The model API name stays the same. Training changes the weights behind it, not the environment or
taskset.

## Inspect the model

Return to the model page after a training step:

| Tab             | What to look for                                                  |
| --------------- | ----------------------------------------------------------------- |
| **Results**     | Evaluation jobs run with this model.                              |
| **Checkpoints** | The checkpoint tree and the weights currently used for inference. |
| **Logs**        | Output from training and model operations.                        |

To inspect checkpoints from the terminal:

```bash theme={"dark"}
hud models checkpoints arith-rl
hud models head arith-rl --set <checkpoint-id>
```

The [training agents guide](/v6/guides/training-agents) contains the complete Python loop. See the
[`TrainingClient` reference](/v6/reference/training#trainingclient) for losses and checkpoint
operations.