Saturday, June 1, 2024

Mobile AI Development: How to Run AI Models on Devices

 This video shows how you can install and run LLMs on smart devices like mobile phone, tablets, IOT devices, drones, cameras, smart watches etc by using Qualcomm AI Hub.


pip install torch
pip install transformers
pip install ipywidgets
pip install psutil
pip install huggingface_hub
pip install pillow
pip install qai-hub
pip install qai_hub_models
pip install "qai_hub_models[yolov7]"

huggingface-cli login

qai-hub configure --api_token <token>

qai-hub list-devices

from PIL import Image

from typing import Tuple
import torch
import qai_hub as hub
from qai_hub_models.models.yolov7 import Model as YOLOv7Model

# Load YOLOv7 in PyTorch
torch_model = YOLOv7Model.from_pretrained()

# Trace the PyTorch model using one data point of provided sample inputs to
# torch tensor to trace the model.
example_input = [torch.tensor(data[0]) for name, data in torch_model.sample_inputs().items()]
pt_model = torch.jit.trace(torch_model, example_input)

# Select a device
device = hub.Device("Samsung Galaxy S23")

# Compile model for a specific device
compile_job = hub.submit_compile_job(

# Get target model to run on a cloud hosted device
target_model = compile_job.get_target_model()

# Profile the previously compiled model on a cloud hosted device
profile_job = hub.submit_profile_job(

# Perform on-device inference on a cloud hosted device
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(

# Returns the output as dict{name: numpy}
on_device_output = inference_job.download_output_data()


1 comment:

Pranesh Umashankar said...

Hi Fahd, love your work and would love to contact you about working with you. How could I get in touch?