Instructions to use ruikangliu/FlatQuant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruikangliu/FlatQuant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ruikangliu/FlatQuant")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ruikangliu/FlatQuant", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and library name; include extended description from Github README
#1
by nielsr HF Staff - opened
This PR adds the pipeline_tag and library_name to the model card, ensuring that the model is discoverable on the Hugging Face Hub.
It also includes a more detailed description from the Github README to provide users with a better understanding of the model, and links to the paper and github repo.
ruikangliu changed pull request status to merged