Instructions to use hmarkc/FW-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hmarkc/FW-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hmarkc/FW-merged")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hmarkc/FW-merged", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add model card
#1
by nielsr HF Staff - opened
This PR adds a model card for the Roberta model checkpoints presented in FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization. It includes relevant metadata such as the pipeline tag, library name and license, and links to the paper and the Github repository.
hmarkc changed pull request status to merged