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README.md

@@ -35,9 +35,12 @@ More details on model performance across various devices, can be found
   - Precision: w8a16 (8-bit weights, 16-bit activations)
 
 
+
+
 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
 | ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 3.253 ms | 0 - 132 MB | INT8 | NPU |  [ConvNext-Tiny-w8a16-Quantized.so](https://huggingface.co/qualcomm/ConvNext-Tiny-w8a16-Quantized/blob/main/ConvNext-Tiny-w8a16-Quantized.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 3.272 ms | 0 - 8 MB | INT8 | NPU |  [ConvNext-Tiny-w8a16-Quantized.so](https://huggingface.co/qualcomm/ConvNext-Tiny-w8a16-Quantized/blob/main/ConvNext-Tiny-w8a16-Quantized.so) 
+
 
 
 ## Installation
@@ -99,89 +102,14 @@ python -m qai_hub_models.models.convnext_tiny_w8a16_quantized.export
 Profile Job summary of ConvNext-Tiny-w8a16-Quantized
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 3.62 ms
-Estimated Peak Memory Range: 0.40-0.40 MB
+Estimated Inference Time: 3.57 ms
+Estimated Peak Memory Range: 0.21-0.21 MB
 Compute Units: NPU (215) | Total (215)
 
 
 ```
-## How does this work?
-
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ConvNext-Tiny-w8a16-Quantized/export.py)
-leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
-on-device. Lets go through each step below in detail:
-
-Step 1: **Compile model for on-device deployment**
-
-To compile a PyTorch model for on-device deployment, we first trace the model
-in memory using the `jit.trace` and then call the `submit_compile_job` API.
-
-```python
-import torch
-
-import qai_hub as hub
-from qai_hub_models.models.convnext_tiny_w8a16_quantized import Model
-
-# Load the model
-torch_model = Model.from_pretrained()
-torch_model.eval()
-
-# Device
-device = hub.Device("Samsung Galaxy S23")
-
-# Trace model
-input_shape = torch_model.get_input_spec()
-sample_inputs = torch_model.sample_inputs()
-
-pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
-
-# Compile model on a specific device
-compile_job = hub.submit_compile_job(
-    model=pt_model,
-    device=device,
-    input_specs=torch_model.get_input_spec(),
-)
 
-# Get target model to run on-device
-target_model = compile_job.get_target_model()
-
-```
-
-
-Step 2: **Performance profiling on cloud-hosted device**
-
-After compiling models from step 1. Models can be profiled model on-device using the
-`target_model`. Note that this scripts runs the model on a device automatically
-provisioned in the cloud.  Once the job is submitted, you can navigate to a
-provided job URL to view a variety of on-device performance metrics.
-```python
-profile_job = hub.submit_profile_job(
-    model=target_model,
-    device=device,
-)
-
-```
-
-Step 3: **Verify on-device accuracy**
-
-To verify the accuracy of the model on-device, you can run on-device inference
-on sample input data on the same cloud hosted device.
-```python
-input_data = torch_model.sample_inputs()
-inference_job = hub.submit_inference_job(
-    model=target_model,
-    device=device,
-    inputs=input_data,
-)
-
-on_device_output = inference_job.download_output_data()
-
-```
-With the output of the model, you can compute like PSNR, relative errors or
-spot check the output with expected output.
 
-**Note**: This on-device profiling and inference requires access to Qualcomm®
-AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
 ## Run demo on a cloud-hosted device
@@ -220,7 +148,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of ConvNext-Tiny-w8a16-Quantized can be found
   [here](https://github.com/pytorch/vision/blob/main/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
+- The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
 ## References
 * [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)