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README.md

@@ -17,11 +17,9 @@ tags:
 
 The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
 
-This model is an implementation of Mistral-7B-Instruct-v0_3 found [here]({source_repo}).
-This repository provides scripts to run Mistral-7B-Instruct-v0_3 on Qualcomm® devices.
-More details on model performance across various devices, can be found
-[here](https://aihub.qualcomm.com/models/mistral_7b_instruct_v0_3_quantized).
-
+This is based on the implementation of Mistral-7B-Instruct-v0_3 found
+[here]({source_repo}). More details on model performance
+accross various devices, can be found [here](https://aihub.qualcomm.com/models/mistral_7b_instruct_v0_3_quantized).
 
 ### Model Details
 
@@ -51,243 +49,6 @@ More details on model performance across various devices, can be found
 
 
 
-## Installation
-
-This model can be installed as a Python package via pip.
-
-```bash
-pip install qai-hub-models
-```
-
-
-## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
-
-Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
-Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
-
-With this API token, you can configure your client to run models on the cloud
-hosted devices.
-```bash
-qai-hub configure --api_token API_TOKEN
-```
-Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
-
-
-
-## Demo on-device
-
-The package contains a simple end-to-end demo that downloads pre-trained
-weights and runs this model on a sample input.
-
-```bash
-python -m qai_hub_models.models.mistral_7b_instruct_v0_3_quantized.demo
-```
-
-The above demo runs a reference implementation of pre-processing, model
-inference, and post processing.
-
-**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
-environment, please add the following to your cell (instead of the above).
-```
-%run -m qai_hub_models.models.mistral_7b_instruct_v0_3_quantized.demo
-```
-
-
-### Run model on a cloud-hosted device
-
-In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
-device. This script does the following:
-* Performance check on-device on a cloud-hosted device
-* Downloads compiled assets that can be deployed on-device for Android.
-* Accuracy check between PyTorch and on-device outputs.
-
-```bash
-python -m qai_hub_models.models.mistral_7b_instruct_v0_3_quantized.export
-```
-```
-Profiling Results
-------------------------------------------------------------
-
-Device                       : Snapdragon 8 Elite QRD (15)
-Runtime                      : QNN                        
-Response Rate (Tokens/Second): 10.73                      
-Time to First Token (Seconds): (0.18, 5.79)               
-```
-
-
-## How does this work?
-
-This [export script](https://aihub.qualcomm.com/models/mistral_7b_instruct_v0_3_quantized/qai_hub_models/models/Mistral-7B-Instruct-v0_3/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: **Upload compiled model**
-
-Upload compiled models from `qai_hub_models.models.mistral_7b_instruct_v0_3_quantized` on hub.
-```python
-import torch
-
-import qai_hub as hub
-from qai_hub_models.models.mistral_7b_instruct_v0_3_quantized import Model
-
-# Load the model
-model = Model.from_precompiled()
-
-model_promptprocessor_part1 = hub.upload_model(model.prompt_processor_part1.get_target_model_path())
-model_promptprocessor_part2 = hub.upload_model(model.prompt_processor_part2.get_target_model_path())
-model_promptprocessor_part3 = hub.upload_model(model.prompt_processor_part3.get_target_model_path())
-model_promptprocessor_part4 = hub.upload_model(model.prompt_processor_part4.get_target_model_path())
-model_tokengenerator_part1 = hub.upload_model(model.token_generator_part1.get_target_model_path())
-model_tokengenerator_part2 = hub.upload_model(model.token_generator_part2.get_target_model_path())
-model_tokengenerator_part3 = hub.upload_model(model.token_generator_part3.get_target_model_path())
-model_tokengenerator_part4 = hub.upload_model(model.token_generator_part4.get_target_model_path())
-```
-
-
-Step 2: **Performance profiling on cloud-hosted device**
-
-After uploading compiled 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
-
-# Device
-device = hub.Device("Samsung Galaxy S23")
-profile_job_promptprocessor_part1 = hub.submit_profile_job(
-    model=model_promptprocessor_part1,
-    device=device,
-)
-profile_job_promptprocessor_part2 = hub.submit_profile_job(
-    model=model_promptprocessor_part2,
-    device=device,
-)
-profile_job_promptprocessor_part3 = hub.submit_profile_job(
-    model=model_promptprocessor_part3,
-    device=device,
-)
-profile_job_promptprocessor_part4 = hub.submit_profile_job(
-    model=model_promptprocessor_part4,
-    device=device,
-)
-profile_job_tokengenerator_part1 = hub.submit_profile_job(
-    model=model_tokengenerator_part1,
-    device=device,
-)
-profile_job_tokengenerator_part2 = hub.submit_profile_job(
-    model=model_tokengenerator_part2,
-    device=device,
-)
-profile_job_tokengenerator_part3 = hub.submit_profile_job(
-    model=model_tokengenerator_part3,
-    device=device,
-)
-profile_job_tokengenerator_part4 = hub.submit_profile_job(
-    model=model_tokengenerator_part4,
-    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_promptprocessor_part1 = model.prompt_processor_part1.sample_inputs()
-inference_job_promptprocessor_part1 = hub.submit_inference_job(
-    model=model_promptprocessor_part1,
-    device=device,
-    inputs=input_data_promptprocessor_part1,
-)
-on_device_output_promptprocessor_part1 = inference_job_promptprocessor_part1.download_output_data()
-
-input_data_promptprocessor_part2 = model.prompt_processor_part2.sample_inputs()
-inference_job_promptprocessor_part2 = hub.submit_inference_job(
-    model=model_promptprocessor_part2,
-    device=device,
-    inputs=input_data_promptprocessor_part2,
-)
-on_device_output_promptprocessor_part2 = inference_job_promptprocessor_part2.download_output_data()
-
-input_data_promptprocessor_part3 = model.prompt_processor_part3.sample_inputs()
-inference_job_promptprocessor_part3 = hub.submit_inference_job(
-    model=model_promptprocessor_part3,
-    device=device,
-    inputs=input_data_promptprocessor_part3,
-)
-on_device_output_promptprocessor_part3 = inference_job_promptprocessor_part3.download_output_data()
-
-input_data_promptprocessor_part4 = model.prompt_processor_part4.sample_inputs()
-inference_job_promptprocessor_part4 = hub.submit_inference_job(
-    model=model_promptprocessor_part4,
-    device=device,
-    inputs=input_data_promptprocessor_part4,
-)
-on_device_output_promptprocessor_part4 = inference_job_promptprocessor_part4.download_output_data()
-
-input_data_tokengenerator_part1 = model.token_generator_part1.sample_inputs()
-inference_job_tokengenerator_part1 = hub.submit_inference_job(
-    model=model_tokengenerator_part1,
-    device=device,
-    inputs=input_data_tokengenerator_part1,
-)
-on_device_output_tokengenerator_part1 = inference_job_tokengenerator_part1.download_output_data()
-
-input_data_tokengenerator_part2 = model.token_generator_part2.sample_inputs()
-inference_job_tokengenerator_part2 = hub.submit_inference_job(
-    model=model_tokengenerator_part2,
-    device=device,
-    inputs=input_data_tokengenerator_part2,
-)
-on_device_output_tokengenerator_part2 = inference_job_tokengenerator_part2.download_output_data()
-
-input_data_tokengenerator_part3 = model.token_generator_part3.sample_inputs()
-inference_job_tokengenerator_part3 = hub.submit_inference_job(
-    model=model_tokengenerator_part3,
-    device=device,
-    inputs=input_data_tokengenerator_part3,
-)
-on_device_output_tokengenerator_part3 = inference_job_tokengenerator_part3.download_output_data()
-
-input_data_tokengenerator_part4 = model.token_generator_part4.sample_inputs()
-inference_job_tokengenerator_part4 = hub.submit_inference_job(
-    model=model_tokengenerator_part4,
-    device=device,
-    inputs=input_data_tokengenerator_part4,
-)
-on_device_output_tokengenerator_part4 = inference_job_tokengenerator_part4.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).
-
-
-
-
-## Deploying compiled model to Android
-
-
-The models can be deployed using multiple runtimes:
-- TensorFlow Lite (`.tflite` export): [This
-  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
-  guide to deploy the .tflite model in an Android application.
-
-
-- QNN ( `.so` / `.bin` export ): This [sample
-  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
-provides instructions on how to use the `.so` shared library or `.bin` context binary in an Android application.
-
-
-## View on Qualcomm® AI Hub
-Get more details on Mistral-7B-Instruct-v0_3's performance across various devices [here](https://aihub.qualcomm.com/models/mistral_7b_instruct_v0_3_quantized).
-Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
-
-
 ## License
 * The license for the original implementation of Mistral-7B-Instruct-v0_3 can be found [here](https://github.com/mistralai/mistral-inference/blob/main/LICENSE).
 * The license for the compiled assets for on-device deployment can be found [here](https://github.com/mistralai/mistral-inference/blob/main/LICENSE)
@@ -301,10 +62,9 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
 
 ## Community
-* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:[email protected]).
 
-
 ## Usage and Limitations
 
 Model may not be used for or in connection with any of the following applications:
@@ -325,5 +85,3 @@ Model may not be used for or in connection with any of the following application
 - Recommender systems of social media platforms;
 - Scraping of facial images (from the internet or otherwise); and/or
 - Subliminal manipulation
-
-