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---
library_name: pytorch
license: other
tags:
- llm
- generative_ai
- android
pipeline_tag: text-generation
---
# Llama-v3.2-3B-Instruct: Optimized for Mobile Deployment
## State-of-the-art large language model useful on a variety of language understanding and generation tasks
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 model is an implementation of Llama-v3.2-3B-Instruct found [here](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct/).
This repository provides scripts to run Llama-v3.2-3B-Instruct on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/llama_v3_2_3b_instruct).
**WARNING**: The model assets are not readily available for download due to licensing restrictions.
### Model Details
- **Model Type:** Model_use_case.text_generation
- **Model Stats:**
- Input sequence length for Prompt Processor: 128
- Maximum context length: 4096
- Precision: w4 + w8 (few layers) with fp16 activations and w4a16 + w8a16 (few layers) are supported
- Num of key-value heads: 8
- Model-1 (Prompt Processor): Llama-PromptProcessor-Quantized
- Prompt processor input: 128 tokens + position embeddings + attention mask + KV cache inputs
- Prompt processor output: 128 output tokens + KV cache outputs
- Model-2 (Token Generator): Llama-TokenGenerator-Quantized
- Token generator input: 1 input token + position embeddings + attention mask + KV cache inputs
- Token generator output: 1 output token + KV cache outputs
- Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
- Minimum QNN SDK version required: 2.27.7
- 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.
| Model | Precision | Device | Chipset | Target Runtime | Response Rate (tokens per second) | Time To First Token (range, seconds)
|---|---|---|---|---|---|
| Llama-v3.2-3B-Instruct | w4a16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | GENIE | 29.48402 | 0.058016 - 1.856531 | -- | -- |
| Llama-v3.2-3B-Instruct | w4a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | GENIE | 23.4718 | 0.088195 - 2.82225 | -- | -- |
| Llama-v3.2-3B-Instruct | w4a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | GENIE | 18.4176 | 0.12593600000000002 - 4.029952000000001 | -- | -- |
| Llama-v3.2-3B-Instruct | w4a16 | SA8255P ADP | Qualcomm® SA8255P | GENIE | 14.02377 | 0.187414 - 5.997256999999999 | -- | -- |
| Llama-v3.2-3B-Instruct | w4 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | GENIE | 18.00883 | 0.131546 - 4.209475 | -- | -- |
| Llama-v3.2-3B-Instruct | w4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | GENIE | 13.83 | 0.088195 - 2.82225 | -- | -- |
| Llama-v3.2-3B-Instruct | w4 | SA8295P ADP | Qualcomm® SA8295P | GENIE | 3.523 | 0.37311700000000003 - 2.9849360000000003 | -- | -- |
## Deploying Llama 3.2 3B on-device
Please follow the [LLM on-device deployment](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial.
## Installation
Install the package via pip:
```bash
# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[llama-v3-2-3b-instruct]"
```
## 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 off target
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.llama_v3_2_3b_instruct.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.llama_v3_2_3b_instruct.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.llama_v3_2_3b_instruct.export
```
## 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` 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 in an Android application.
## View on Qualcomm® AI Hub
Get more details on Llama-v3.2-3B-Instruct's performance across various devices [here](https://aihub.qualcomm.com/models/llama_v3_2_3b_instruct).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## 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).
* The license for the compiled assets for on-device deployment 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:[email protected]).
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