README.md

---
library_name: pytorch
license: other
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_2_3b_instruct/web-assets/model_demo.png)

# Llama-v3.2-3B-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.

This is based on the implementation of Llama-v3.2-3B-Instruct found [here](https://huggingface.co/meta-llama/Llama-3.2-3B-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/main/src/qai_hub_models/models/llama_v3_2_3b_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 3.2 3B on-device

Please follow the [LLM on-device deployment](https://github.com/qualcomm/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial.

## 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/main/src/qai_hub_models/models/llama_v3_2_3b_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-v3.2-3B-Instruct on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/llama_v3_2_3b_instruct) for usage instructions.


## Model Details

**Model Type:** Model_use_case.text_generation

**Model Stats:**
- Input sequence length for Prompt Processor: 128
- Maximum context length: 4096
- Quantization Type: w4 + w8 (few layers) with fp16 activations and w4a16 + w8a16 (few layers) are supported
- 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).
- Response Rate: Rate of response generation after the first response token.

## Performance Summary
| Model | Runtime | Precision | Chipset | Context Length | Response Rate (tokens per second) | Time To First Token (range, seconds)
|---|---|---|---|---|---|---
| Llama-v3.2-3B-Instruct | GENIE | w4 | Snapdragon® 8 Elite Gen 5 Mobile | 4096 | 18.00883 | 0.131546 - 4.209475
| Llama-v3.2-3B-Instruct | GENIE | w4 | Snapdragon® 8 Elite Mobile | 4096 | 13.83 | 0.088195 - 2.82225
| Llama-v3.2-3B-Instruct | GENIE | w4 | Qualcomm® SA8295P | 1024 | 3.523 | 0.37311700000000003 - 2.9849360000000003
| Llama-v3.2-3B-Instruct | GENIE | w4a16 | Snapdragon® 8 Elite Gen 5 Mobile | 4096 | 32.65 | 0.06895399999999999 - 2.2065279999999996
| Llama-v3.2-3B-Instruct | GENIE | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 28.03 | 0.08204900000000001 - 2.6255680000000003
| Llama-v3.2-3B-Instruct | GENIE | w4a16 | Snapdragon® X2 Elite | 4096 | 42.77 | 0.075045 - 2.40144
| Llama-v3.2-3B-Instruct | GENIE | w4a16 | Snapdragon® X Elite | 4096 | 11.87 | 0.116884 - 3.740288
| Llama-v3.2-3B-Instruct | GENIE | w4a16 | Qualcomm® SA8775P | 4096 | 17.47 | 0.109614 - 3.507648
| Llama-v3.2-3B-Instruct | GENIE | w4a16 | Qualcomm® QCS9075 | 4096 | 17.757809543609618 | 0.1072016 - 3.4304512

## License
* The license for the original implementation of Llama-v3.2-3B-Instruct can be found
  [here](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct/blob/main/LICENSE.txt).

## References
* [LLaMA: Open and Efficient Foundation Language Models](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_2/)
* [Source Model Implementation](https://huggingface.co/meta-llama/Llama-3.2-3B-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