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Qualcomm AI Hub Models

A collection of machine learning models optimized and ready to deploy on Qualcomm devices, with a CLI to browse, compile, quantize, profile and run them on real hardware.

Qualcomm AI Hub Models

Qualcomm AI Hub Models is a collection of state-of-the-art machine learning models optimized for deployment on Qualcomm devices, distributed as a Python package (qai_hub_models) with a command-line interface for browsing and downloading the collection, plus a Python API. Model READMEs list any extra dependencies a specific model needs and cover supported on-device runtimes, hardware targets and precisions, chipsets, and devices.

Using Qualcomm AI Hub Workbench, which requires a free Qualcomm account and an API token, a model can be exported end to end: compiled for a chosen device and target runtime, optionally quantized, profiled on a real device in the cloud, run with sample input so its on-device output can be compared with the original PyTorch output, and downloaded as a compiled model. Most models also ship a CLI demo that runs the full pipeline end to end, for example predicting and drawing bounding boxes for an object-detection model.

It is released under the BSD-3-Clause license.

More in Models

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  • mini-AGI

    github.com

    A continual learning language model that trains from scratch on a single 8 GB VRAM GPU, assembling its own architecture and adapting its size to the hardware and data available rather than shipping as a fixed pretrained model.

  • An open-source contrastive-embedding pipeline that maps technical CVE vulnerability descriptions to formal cybersecurity compliance controls in NIST SP 800-53 and CMMC, published with its model generation, fine-tuning and evaluation code.

  • peekaboolean

    github.com

    A small vision-language model, fine-tuned with LoRA on a 500-million-parameter backbone, that answers typed multiple-choice, ordinal-score and yes/no questions about an image with calibrated probabilities, in about 400 milliseconds on a laptop.