Llama3-TAIDE-LX-8B-Chat-Alpha1: Optimized for Mobile Deployment

State-of-the-art large language model useful on a variety of language understanding and generation tasks

The Llama3-TAIDE-LX-8B-Chat-Alpha1 LLM model is based on Meta's released LLaMA3-8b model, fine-tuned on Traditional Chinese data, and enhanced for office tasks and multi-turn dialogue capabilities through instruction tuning. The TAIDE model is incorporating text and training materials from various fields in Taiwan to enhance the model's ability to respond in Traditional Chinese and perform specific tasks such as automatic summarization, writing emails, articles, and translating between Chinese and English.

This model is an implementation of Llama3-TAIDE-LX-8B-Chat-Alpha1 found here.

This repository provides scripts to run Llama3-TAIDE-LX-8B-Chat-Alpha1 on Qualcomm® devices. More details on model performance across various devices, can be found here.

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: w4a16 + w8a16 (few layers)
- 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, Traditional Chinese
- 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) |---|---|---|---|---|---| | Llama3-TAIDE-LX-8B-Chat-Alpha1 | w4a16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | GENIE | 14.91757 | 0.109992 - 3.519753 | -- | -- | | Llama3-TAIDE-LX-8B-Chat-Alpha1 | w4a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | GENIE | 12.9262 | 0.159383 - 5.100256 | -- | -- | | Llama3-TAIDE-LX-8B-Chat-Alpha1 | w4a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | GENIE | 10.0367 | 0.211644 - 6.772608 | -- | -- |

Deploying Llama-3-TAIDE on-device

Please follow the LLM on-device deployment tutorial.

Installation

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[llama-v3-taide-8b-chat]"

Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub Workbench 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.

qai-hub configure --api_token API_TOKEN

Navigate to 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.

python -m qai_hub_models.models.llama_v3_taide_8b_chat.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_taide_8b_chat.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.

python -m qai_hub_models.models.llama_v3_taide_8b_chat.export

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.
QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on Llama3-TAIDE-LX-8B-Chat-Alpha1's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of Llama3-TAIDE-LX-8B-Chat-Alpha1 can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
For questions or feedback please reach out to us.

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