--- library_name: pytorch license: llama3 pipeline_tag: text-generation tags: - llm - generative_ai - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/llama_v3_8b_chat_quantized/web-assets/model_demo.png) # Llama-v3-8B-Chat: 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 "Chat" at the end indicates that the model is optimized for chatbot-like dialogue. 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-v3-8B-Chat found [here]({source_repo}). This repository provides scripts to run Llama-v3-8B-Chat on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/llama_v3_8b_chat_quantized). ### Model Details - **Model Type:** Text generation - **Model Stats:** - Context length: 4096 - Number of parameters: 8B - Model size: 4.8GB - 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. ## Deploying Llama 3 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. ## Sample output prompts generated on-device 1. --prompt "where is California?" ``` ------- Response Summary -------- Prompt: where is California? Response: California is a state located on the West Coast of ``` 2. --prompt "what is 2+3?" --max-output-tokens 30 ``` -------- Response Summary -------- Prompt: what is 2+3? Response: 2 + 3 = 5 ``` 3. --prompt "what is superposition in Quantum Physics?" --max-output-tokens 30 ``` Prompt: what is superposition in Quantum Physics? Response: Superposition is a fundamental concept in quantum mechanics, which is a branch of physics that studies the behavior of matter and energy at a very ``` | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model | ---|---|---|---|---|---|---|---| ## Installation This model can be installed as a Python package via pip. ```bash pip install "qai-hub-models[llama_v3_8b_chat_quantized]" ``` ## 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_8b_chat_quantized.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_8b_chat_quantized.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_8b_chat_quantized.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-v3-8B-Chat's performance across various devices [here](https://aihub.qualcomm.com/models/llama_v3_8b_chat_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Llama-v3-8B-Chat can be found [here](https://github.com/facebookresearch/llama/blob/main/LICENSE). * The license for the compiled assets for on-device deployment can be found [here](https://github.com/facebookresearch/llama/blob/main/LICENSE) ## References * [LLaMA: Open and Efficient Foundation Language Models](https://ai.meta.com/blog/meta-llama-3/) * [Source Model Implementation](https://github.com/meta-llama/llama3/tree/main) ## 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).