diff --git a/README.md b/README.md
index 54ee08b..9465618 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ RIA_Example/
 │   └── example_synthetic_dataset.h5      # Synthetically generated dataset (Generator output)
 │
 ├── models/
-│   ├── example_model.pt                  # PyTorch Module (Model Trainer input / output)
+│   ├── example_model.ckpt                  # PyTorch Module (Model Trainer input / output)
 │   └── example_model.onnx               # Exported ONNX model (Screens / Application Packager input)
 │
 ├── applications/
@@ -67,9 +67,9 @@ The Library is a cross-repository browser for all RF and ML assets on the platfo
 |------|-----------|-------------|
 | Recording | `.h5` / `.hdf5` | Raw IQ capture files |
 | Radio Dataset | `.h5` / `.hdf5` | Labelled, curated training datasets |
-| PyTorch Module | `.pt` / `.pth` | Serialized PyTorch models |
-| PyTorch State Dict | `.pt` / `.pth` | Model weight dictionaries |
-| PyTorch Checkpoint | `.pt` / `.pth` | Mid-training checkpoints |
+| PyTorch Module | `.py` | PyTorch model definitions with a nn.Module class |
+| PyTorch State Dict | `.pt` / `.pth` | Model weights / state dictionaries |
+| PyTorch Checkpoint | `.ckpt` | Training checkpoints with weights, optimizer state, and metadata |
 | ONNX Graph | `.onnx` | Portable inference models |
 
 ---
@@ -153,13 +153,13 @@ The Generator creates synthetic labelled datasets from a parameter sweep without
 
 The Model Trainer builds a training workflow YAML and commits it to your repository. A Gitea Actions runner then executes the training job.
 
-**Example files:** `datasets/example_radio_dataset.h5`, `models/example_model.pt` (optional pre-trained start)  
-**Expected output:** `.riahub/workflows/train.yaml` in your repository, plus a trained `example_model.pt` artifact
+**Example files:** `datasets/example_radio_dataset.h5`, `models/example_model.ckpt` (optional pre-trained start)  
+**Expected output:** `.riahub/workflows/train.yaml` in your repository, plus a trained `example_model.ckpt` artifact
 
 **Steps:**
 1. Go to **Model Builder → Model Trainer**.
 2. In **Repository**, select the repository where you want to store the workflow and output artifacts.
-3. In **Model**, choose an architecture (e.g. `ResNet1D`) or use `example_model.pt` as a starting checkpoint.
+3. In **Model**, choose an architecture (e.g. `ResNet1D`) or use `example_model.ckpt` as a starting checkpoint.
 4. In **Dataset**, select `example_radio_dataset.h5` from the Library.
 5. Configure training:
    - **Optimizer:** `Adam`, learning rate `1e-3`
@@ -197,12 +197,12 @@ HPO runs a sweep over a configurable search space, training multiple model varia
 
 Compression applies pruning and/or quantization to reduce model size for edge deployment. The output is an ONNX file.
 
-**Example files:** `models/example_model.pt`, `datasets/example_radio_dataset.h5`  
+**Example files:** `models/example_model.ckpt`, `datasets/example_radio_dataset.h5`  
 **Expected output:** `models/example_model.onnx`
 
 **Steps:**
 1. Go to **Model Builder → Compression**.
-2. Select `example_model.pt` as the source model and `example_radio_dataset.h5` as the calibration dataset.
+2. Select `example_model.ckpt` as the source model and `example_radio_dataset.h5` as the calibration dataset.
 3. Configure the compression pipeline (pruning ratio, quantization bits).
 4. Click **Commit Workflow**. The Actions job exports the compressed model to ONNX automatically.
 5. The resulting `.onnx` file is committed back to your repository.