Upload README.md with huggingface_hub

README.md

@@ -125,11 +125,9 @@ This [export script](https://aihub.qualcomm.com/models/whisper_tiny_en/qai_hub_m
 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.
+Step 1: **Upload compiled model**
 
+Upload compiled models from `qai_hub_models.models.whisper_tiny_en` on hub.
 ```python
 import torch
 
@@ -137,27 +135,10 @@ import qai_hub as hub
 from qai_hub_models.models.whisper_tiny_en import Model
 
 # Load the model
-torch_model = Model.from_pretrained()
-
-# 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()
+model = Model.from_precompiled()
 
+model_WhisperEncoder = hub.upload_model(model.encoder.get_target_model_path())
+model_WhisperDecoder = hub.upload_model(model.decoder.get_target_model_path())
 ```