This repository was archived by the owner on Apr 24, 2025. It is now read-only.
Example script for running inference on common hugging face models #38
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
The script is intended to show a developer usage example. Notebook will download common use-case models from Hugging Face and use the intel_npu_acceleration_library to run inference on NPU. Notebook was developed and tested on an Intel Ultra 7 155H device.
Sign up for free
to subscribe to this conversation on GitHub.
Already have an account?
Sign in.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The Jupyter notebook is intended to serve as an example to use the intel_npu_acceleration_library for running inference on common text to text, audio to text and visual QA models from hugging face.
The configuration TOML can be used to specify flags and parameters for running inference on such models.