Upload Your Models
==================

Supported Model Formats
-----------------------

**eIQ AI Hub** accepts a range of common model formats. After upload, your model is automatically
validated and inspected to ensure compatibility with downstream tools such as converters,
the Optimizer, and profilers.

The supported file formats are:

* **TensorFlow Lite** (``.tflite``) — float32 or quantized (int8/uint8)
* **ONNX** (``.onnx``)
* **PyTorch** (``.zip`` archive containing the model)

Upload Workflow
---------------

The model upload process follows a three-step wizard:

1. **Select File** — choose a model file from your local filesystem.
2. **Review** — verify and edit the auto-detected model information (name, type, inputs, outputs).
3. **Upload** — confirm and submit the model to your workspace.

.. note::
   The system automatically detects the model name and type from the uploaded file.
   It also detects NPU optimization support (Neutron SDK/Vela) from the model.

Upload TensorFlow Lite Model
----------------------------

You can upload either a float32 model or a quantized model (int8/uint8) in ``.tflite`` format.

.. image:: /_static/eIQ_AIHub_upload_tflite.gif
   :alt: Upload TFLite Model
   :width: 100%

**Steps:**

1. In the left navigation menu, click **Models** > **Upload Model**.
2. On the **Select File** step, drag and drop the ``.tflite`` file into the upload area,
   or click to browse your local filesystem.
3. On the **Review** step, verify the auto-detected model information:

   - **Model Name** — edit if needed.
   - **Type** — confirmed as ``tflite``.
   - **Description** — optional; describe your model's purpose.
   - **Task Type** — optional; select the primary task (e.g., Image Classification).
   - **Architecture** — optional; select the model architecture.
   - **Model Inputs** — verify the input node names, shapes, and data types.
   - **Model Outputs** — verify the output node names and shapes.

4. On the **Upload** step, review the summary (model name, type, file) and click **Upload Model**.

After uploading, the model appears in your model list. Click the model to view its detail page,
where you can inspect the model visualization, download the model file, or delete it.

Upload ONNX Model
------------------

Upload an ONNX model in ``.onnx`` format.

.. image:: /_static/eIQ_AIHub_upload_onnx.gif
   :alt: Upload ONNX Model
   :width: 100%

**Steps:**

1. In the left navigation menu, click **Models** > **Upload Model**.
2. On the **Select File** step, drag and drop the ``.onnx`` file into the upload area,
   or click to browse your local filesystem.
3. On the **Review** step, verify the auto-detected model information (name, type, inputs, outputs).
4. On the **Upload** step, review the summary and click **Upload Model**.

Upload PyTorch Model
---------------------

PyTorch models must be packaged as a ``.zip`` archive before uploading.

.. image:: /_static/AI_Hub_Tutorial_Upload_pytorch_model.gif
   :alt: Upload PyTorch Model
   :width: 100%

**Steps:**

In this workflow, PyTorch models require separate model definition and weight files, unlike TFLite and ONNX models, which typically store both in a single file.
Users need to prepare:

* **model.py** for the model definition
* a weight file such as ***.pt**

1. Package your PyTorch model files into a ``.zip`` archive.
2. In the left navigation menu, click **Models** > **Upload Model**.
3. On the **Select File** step, drag and drop the ``.zip`` file into the upload area,
   or click to browse your local filesystem.
4. On the **Review** step, verify and edit the model information:

   - **Model Name** — edit if needed (auto-detected from the archive).
   - **Type** — confirmed as ``pytorch``.
   - **Description** — optional.
   - **Task Type** — optional.
   - **Architecture** — optional.
   - **Model Inputs** — specify input node names, shapes (e.g., ``1,224,224,3``), and data types.
   - **Model Outputs** — specify output node names and shapes.

5. On the **Upload** step, review the summary and click **Upload Model**.


Next Steps
----------

* :doc:`Upload your dataset<./upload_dataset>`
* :doc:`Optimize your model<./optimize>`