Instructions to use leafyseay/RIME-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leafyseay/RIME-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="leafyseay/RIME-7B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("leafyseay/RIME-7B") model = AutoModelForMultimodalLM.from_pretrained("leafyseay/RIME-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and library name metadata
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face team.
This PR improves the model card for RIME-7B by:
- Adding the
pipeline_tag: feature-extractionmetadata so that the model is properly categorized and discoverable. - Adding
library_name: transformersmetadata since this model usesQwen2VLForConditionalGenerationfrom thetransformerslibrary. - Adding a link to the paper page on Hugging Face to make it easy for users to find the original research.
Let me know if you have any questions!
leafyseay changed pull request status to merged