# Models

The **Models** section holds everything in your model catalogue: base models you've uploaded, fine-tuned outputs from training jobs, and checkpoints. It mirrors `client.models` in the [SDK](/docs/sdk).

## Model types

Models are grouped by source:

- **Uploaded:** base models you brought in from a local directory or HuggingFace.
- **Fine-tuned:** output models produced by a completed training job.
- **Checkpoints:** intermediate snapshots saved during training. See [Storage](/docs/storage) for how the checkpoint limit keeps these in check.

## Add a model

Click **Upload model** to bring in a base model, either from a HuggingFace repo ID (e.g. `meta-llama/Llama-3.2-1B`) or a local directory. Fleet computes a content hash first, so re-uploading the same model is instant and free.

## Train a model

Click **Train** on any model to open the training console.

1. **Method:** choose `full`, `lora`, `qlora`, or `dpo`. Each shows a short description of the trade-off.
2. **GPU tier:** defaults to **Auto**, which lets Fleet pick a GPU that fits the model and is the most cost-effective for the run. The exact tier depends on live availability when the job starts. To pin a specific tier, click **Choose a tier myself**.
3. **Dataset:** pick an uploaded dataset (optional, but required for a cost estimate).
4. **Hyperparameters:** sensible defaults are pre-filled; expand **Advanced** to override any of them. See [Training](/docs/training) for the full list.

Once a dataset is selected, the console shows a live **estimate** for each tier: VRAM required, estimated time, and estimated cost. Tiers that can't fit the model are greyed out. Hit **Start training** to launch; you'll get the job ID, the assigned GPU, and the VRAM and cost estimate on the confirmation screen.

## Deploy a model

Click **Deploy** on a ready model to spin up an inference endpoint. Pick the hardware tier and Fleet provisions a serverless endpoint that scales to zero when idle. See [Deployments](/docs/dashboard-deployments).

## Manage models

Each model row links to its details and offers actions to download (via a signed URL) or delete it. Deleting a model frees its storage immediately.