diff --git a/src/brevitas_examples/super_resolution/README.md b/src/brevitas_examples/super_resolution/README.md
index 9e0068c27..1c73873e5 100644
--- a/src/brevitas_examples/super_resolution/README.md
+++ b/src/brevitas_examples/super_resolution/README.md
@@ -1,6 +1,6 @@
 # Integer-Quantized Super Resolution Experiments with Brevitas
 
-This directory contains scripts demonstrating how to train integer-quantized super resolution models using [Brevitas](https://github.com/Xilinx/brevitas).
+This directory contains scripts demonstrating how to train integer-quantized super resolution models using Brevitas.
 Code is also provided to demonstrate accumulator-aware quantization (A2Q) as proposed in our ICCV 2023 paper "[A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance](https://arxiv.org/abs/2308.13504)".
 
 ## Experiments
@@ -12,25 +12,32 @@ During inference center cropping is applied.
 Inputs are then downscaled by 2x and then used to train the model directly in the RGB space.
 Note that this is a difference from many academic works that train only on the Y-channel in YCbCr format.
 
-| Model Name                  | Upscale Factor | Weight quantization | Activation quantization | Peak Signal-to-Noise Ratio |
+| Model Name | Upscale Factor | Weight quantization | Activation quantization | Peak Signal-to-Noise Ratio |
 |-----------------------------|----------------|---------------------|-------------------------|----------------------------|
-| bicubic_interp              | x2             | N/A                 | N/A                     | 28.71                      |
-| [float_espcn_x2]()          | x2             | float32             | float32                 | 31.03                      |
+| bicubic_interp | x2 | N/A | N/A | 28.71 |
+| [float_espcn_x2](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/float_espcn_x2-2f85a454.pth) | x2 | float32 | float32 | 31.03 |
 ||
-| [quant_espcn_x2_w8a8_base]()    | x2             | int8                | (u)int8                 | 30.96                  |
-| [quant_espcn_x2_w8a8_a2q_32b]() | x2             | int8                | (u)int8                 | 30.79                  |
-| [quant_espcn_x2_w8a8_a2q_16b]() | x2             | int8                | (u)int8                 | 30.56                  |
+| [quant_espcn_x2_w8a8_base](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w8a8_base-f761e4a1.pth) | x2 | int8 | (u)int8 | 30.96 |
+| [quant_espcn_x2_w8a8_a2q_32b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w8a8_a2q_32b-85470d9b.pth) | x2 | int8 | (u)int8 | 30.79 |
+| [quant_espcn_x2_w8a8_a2q_16b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w8a8_a2q_16b-f9e1da66.pth) | x2 | int8 | (u)int8 | 30.56 |
 ||
-| [quant_espcn_x2_w4a4_base]()    | x2             | int4                | (u)int4                 | 30.30                  |
-| [quant_espcn_x2_w4a4_a2q_32b]() | x2             | int4                | (u)int4                 | 30.27                  |
-| [quant_espcn_x2_w4a4_a2q_13b]() | x2             | int4                | (u)int4                 | 30.24                  |
+| [quant_espcn_x2_w4a4_base](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w4a4_base-80658e6d.pth) | x2 | int4 | (u)int4 | 30.30 |
+| [quant_espcn_x2_w4a4_a2q_32b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w4a4_a2q_32b-8702a412.pth) | x2 | int4 | (u)int4 | 30.27 |
+| [quant_espcn_x2_w4a4_a2q_13b](https://github.com/Xilinx/brevitas/releases/download/super_res_r1/quant_espcn_x2_w4a4_a2q_13b-9fff234e.pth) | x2 | int4 | (u)int4 | 30.24 |
 
 
 ## Train
 
-To start training a model from scratch (*e.g.*, `quant_espcn_x2_w8a8_a2q_32b`) run:
+All models are trained from scratch as follows:
  ```bash
-python train_model.py --data_root=data --model=quant_espcn_x2_w8a8_a2q_32b
+python train_model.py^
+    --data_root=./data^
+    --model=quant_espcn_x2_w8a8_a2q_32b^
+    --batch_size=8^
+    --learning_rate=0.001^
+    --weight_decay=0.00001^
+    --gamma=0.999^
+    --step_size=1
  ```
 
 ## Evaluate
diff --git a/src/brevitas_examples/super_resolution/models/__init__.py b/src/brevitas_examples/super_resolution/models/__init__.py
index 872624a85..6d533fccb 100644
--- a/src/brevitas_examples/super_resolution/models/__init__.py
+++ b/src/brevitas_examples/super_resolution/models/__init__.py
@@ -45,13 +45,16 @@
             act_bit_width=4,
             acc_bit_width=13)}
 
-root_url = 'https://github.com/Xilinx/brevitas/releases/download/super_res-r0'
+root_url = 'https://github.com/Xilinx/brevitas/releases/download/super_res_r1'
 
 model_url = {
-    'float_espcn_x2': f'{root_url}/float_espcn_x2-2f3821e3.pth',
-    'quant_espcn_x2_w8a8_base': f'{root_url}/quant_espcn_x2_w8a8_base-7d54e29c.pth',
-    'quant_espcn_x2_w8a8_a2q_32b': f'{root_url}/quant_espcn_x2_w8a8_a2q_32b-0b1f361d.pth',
-    'quant_espcn_x2_w8a8_a2q_16b': f'{root_url}/quant_espcn_x2_w8a8_a2q_16b-3c4acd35.pth'}
+    'float_espcn_x2': f'{root_url}/float_espcn_x2-2f85a454.pth',
+    'quant_espcn_x2_w4a4_a2q_13b': f'{root_url}/quant_espcn_x2_w4a4_a2q_13b-9fff234e.pth',
+    'quant_espcn_x2_w4a4_a2q_32b': f'{root_url}/quant_espcn_x2_w4a4_a2q_32b-8702a412.pth',
+    'quant_espcn_x2_w4a4_base': f'{root_url}/quant_espcn_x2_w4a4_base-80658e6d.pth',
+    'quant_espcn_x2_w8a8_a2q_16b': f'{root_url}/quant_espcn_x2_w8a8_a2q_16b-f9e1da66.pth',
+    'quant_espcn_x2_w8a8_a2q_32b': f'{root_url}/quant_espcn_x2_w8a8_a2q_32b-85470d9b.pth',
+    'quant_espcn_x2_w8a8_base': f'{root_url}/quant_espcn_x2_w8a8_base-f761e4a1.pth'}
 
 
 def get_model_by_name(name: str, pretrained: bool = False) -> Union[FloatESPCN, QuantESPCN]: