README.md

---
library_name: pytorch
license: llama3
tags:
- llm
- generative_ai
- android
pipeline_tag: text-generation

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/llama_v3_8b_instruct/web-assets/model_demo.png)

# Llama-v3-8B-Instruct: Optimized for Qualcomm Devices

Llama 3 is a family of LLMs. The model is quantized to w4a16 (4-bit weights and 16-bit activations) and part of the model is quantized to w8a16 (8-bit weights and 16-bit activations) making it suitable for on-device deployment. For Prompt and output length specified below, the time to first token is Llama-PromptProcessor-Quantized's latency and average time per addition token is Llama-TokenGenerator-Quantized's latency.

This is based on the implementation of Llama-v3-8B-Instruct found [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.63.0/src/qai_hub_models/models/llama_v3_8b_instruct) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Deploying Llama-v3-8B-Instruct on-device

Follow the [GenieX quickstart](https://geniex.aihub.qualcomm.com/en/get-started/quickstart) to install GenieX and deploy the model on a target device.

You'll need to export the model artifact using the steps below, then follow [Run a Local Model with GenieX](https://geniex.aihub.qualcomm.com/en/run/cli/quickstart/#run-a-local-model).

See the [LLM-on-Genie](https://github.com/qualcomm/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial to run with the Genie runtime. Note: Genie support will be deprecated soon.

## Getting Started
Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.63.0/src/qai_hub_models/models/llama_v3_8b_instruct) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

See our repository for [Llama-v3-8B-Instruct on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.63.0/src/qai_hub_models/models/llama_v3_8b_instruct) for usage instructions.


## Model Details

**Model Type:** Model_use_case.text_generation

**Model Stats:**
- Response Rate: Rate of response generation after the first response token.
- Supported languages: English.
- TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt. The lower bound is for a short prompt (up to 128 tokens, i.e., one iteration of the prompt processor) and the upper bound is for a prompt using the full context length (4096 tokens).

## Performance Summary
| Model | Runtime | Precision | Chipset | Context Length | Response Rate (tokens per second) | Time To First Token (range, seconds)
|---|---|---|---|---|---|---
| Llama-v3-8B-Instruct | GENIE | w4a16 | Snapdragon® X2 Elite | 4096 | 19.47 | 0.147975 - 4.7352
| Llama-v3-8B-Instruct | GENIE | w4a16 | Snapdragon® X Elite | 4096 | 4.642633438110352 | 0.20893 - 6.68576
| Llama-v3-8B-Instruct | GENIE | w4a16 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 10.764844226837159 | 0.18326900000000002 - 5.8646080000000005
| Llama-v3-8B-Instruct | GENIE | w4a16 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 4.642633438110352 | 0.20893 - 6.68576
| Llama-v3-8B-Instruct | GENIE | w4a16 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 15.001426887512206 | 0.1370342 - 4.3850944
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 4096 | 9.924027 | 0.1662003870967742 - 5.3184123870967746
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® 8 Elite For Galaxy Mobile | 4096 | 9.728168 | 0.19540596774193547 - 6.252990967741935
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® X2 Elite | 4096 | 21.24863 | 0.11502590322580644 - 3.680828903225806
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® X Elite | 4096 | 11.330596 | 0.22880099999999998 - 7.321631999999999
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-8275 | 4096 | 9.875264 | 0.220166 - 7.045312
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 9.6774 | 0.20317929032258064 - 6.5017372903225805
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 11.330596 | 0.22880099999999998 - 7.321631999999999
| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 9.728168 | 0.19540596774193547 - 6.252990967741935

## License
* The license for the original implementation of Llama-v3-8B-Instruct can be found
  [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/LICENSE).

## References
* [LLaMA: Open and Efficient Foundation Language Models](https://ai.meta.com/blog/meta-llama-3/)
* [Source Model Implementation](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/)

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

## Usage and Limitations

This 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