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README.md

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+---
+library_name: pytorch
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- llm
+- generative_ai
+- quantized
+- android
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mistral_7b_instruct_v0_3_quantized/web-assets/model_demo.png)
+
+# Mistral-7B-Instruct-v0_3: Optimized for Mobile Deployment
+## State-of-the-art large language model useful on a variety of language understanding and generation tasks
+
+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).
+
+
+### Model Details
+
+- **Model Type:** Text generation
+- **Model Stats:**
+  - Number of parameters: 7.3B
+  - Precision: w8a16
+  - Num of key-value heads: 8
+  - Information about the model: ['Prompt Processor and Token Generator are split into 4 parts each.', 'Each corresponding Prompt Processor and Token Generator share weights.']
+  - Max context length: 4096
+  - Prompt processor model size: 4.17 GB
+  - Prompt processor input: 128 tokens + KVCache initialized with pad token
+  - Prompt processor output: 128 output tokens + KVCache for token generator
+  - Token generator model size: 4.17 GB
+  - Token generator input: 1 input token + past KVCache
+  - Token generator output: 1 output token + KVCache for next iteration
+  - Decoding length: 4096
+  - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
+
+| Model | Device | Chipset | Target Runtime | Response Rate (Tokens/Second) | Time To First Token Range (Seconds) | Tiny MMLU 
+|---|---|---|---|---|---|---|
+| Mistral-7B-Instruct-v0_3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 10.73 | 180000 - 5790000 | (180000, 5790000) | 58.85% | Use Export Script |
+
+## Deploying Mistral 7B Instruct v3.0 on-device
+  Please follow [this tutorial](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llama)
+  to compile QNN binaries and generate bundle assets to run [ChatApp on Windows](https://github.com/quic/ai-hub-apps/tree/main/apps/windows/cpp/ChatApp) and on Android powered by QNN-Genie.
+
+
+
+## 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): (180000, 5790000)          
+```
+
+
+## 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)
+
+
+
+## References
+* [Mistral 7B](https://arxiv.org/abs/2310.06825)
+* [Source Model Implementation](https://github.com/mistralai/mistral-inference)
+
+
+
+## Community
+* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) 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:
+
+- Accessing essential private and public services and benefits;
+- Administration of justice and democratic processes;
+- Assessing or recognizing the emotional state of a person;
+- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
+- Education and vocational training;
+- Employment and workers management;
+- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
+- General purpose social scoring;
+- Law enforcement;
+- Management and operation of critical infrastructure;
+- Migration, asylum and border control management;
+- Predictive policing;
+- Real-time remote biometric identification in public spaces;
+- Recommender systems of social media platforms;
+- Scraping of facial images (from the internet or otherwise); and/or
+- Subliminal manipulation
+
+