---
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 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

- **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 per second) | Time To First Token (range, seconds) | Tiny MMLU |
|---|---|---|---|---|---|---|
| Mistral-7B-Instruct-v0_3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 10.73 | 0.18 - 5.79 | 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.



## 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://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:ai-hub-support@qti.qualcomm.com).

## 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