--- 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/falcon_v3_7b_instruct/web-assets/model_demo.png) # Falcon3-7B-Instruct: Optimized for Mobile Deployment ## State-of-the-art large language model useful on a variety of language understanding and generation tasks Falcon3 family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B. This model is an implementation of Falcon3-7B-Instruct found [here](https://huggingface.co/tiiuae/Falcon3-7B-Instruct). This repository provides scripts to run Falcon3-7B-Instruct on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/falcon_v3_7b_instruct). ### Model Details - **Model Type:** Model_use_case.text_generation - **Model Stats:** - Input sequence length for Prompt Processor: 128 - Context length: 4096 - Precision: w4a16 + w8a16 (few layers) - Num of key-value heads: 4 - Model-1 (Prompt Processor): PromptProcessor - 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): TokenGenerator - 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. - Supported languages: English, French, Spanish, Portuguese. - Minimum QNN SDK version required: 2.28.2 - 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) |---|---|---|---|---|---| | Falcon3-7B-Instruct | w4a16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | GENIE | 15.8303 | 0.10903 - 3.488966 | -- | Use Export Script | | Falcon3-7B-Instruct | w4a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | GENIE | 14.02985 | 0.1265205 - 4.048656 | -- | Use Export Script | | Falcon3-7B-Instruct | w4a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | GENIE | 9.96829 | 0.1973798 - 6.3161536 | -- | Use Export Script | ## Deploying Falcon3-7B-Instruct 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[falcon-v3-7b-instruct]" ``` ## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.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://workbench.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.falcon_v3_7b_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.falcon_v3_7b_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.falcon_v3_7b_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 Falcon3-7B-Instruct's performance across various devices [here](https://aihub.qualcomm.com/models/falcon_v3_7b_instruct). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Falcon3-7B-Instruct can be found [here](https://falconllm.tii.ae/falcon-terms-and-conditions.html). * The license for the compiled assets for on-device deployment can be found [here](https://falconllm.tii.ae/falcon-terms-and-conditions.html) ## References * [Source Model Implementation](https://huggingface.co/tiiuae/Falcon3-7B-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).