Nomic-Embed-Text Example

This document describes how to use the QAI AppBuilder Python API to run inference with the Nomic-Embed-Text text encoding model on Qualcomm® Hexagon™ Processor (NPU).

Supported devices

Device	SoC
Fogwise® AIRbox Q900	QCS9075

Install QAI AppBuilder

tip

1. Install QAI AppBuilder by following the QAI AppBuilder installation guide.

2. Configure ADSP environment variables as described in Create ADSP environment variables.

Run the sample

Install dependencies

Install sample dependencies in the activated virtual environment:

Device

pip3 install requests tqdm qai-hub py3-wget Pillow torch torchvision opencv-python-headless transformers

Run the script

• Enter the upstream samples directory

  Device

  cd qai-appbuilder/samples

• Run inference

  Device

  python3 Multimodal/Text_Generation/nomic_embed_text/nomic_embed_text.py --chipset 9075

On success the terminal prints an embedding vector (float array). The first run downloads the model and may also fetch Hugging Face tokenizer files.

tip

Launcher alternative from samples:

python3 run_inference.py --list
python3 run_inference.py --model nomic_embed_text --args "--chipset 9075"