Instructions to use jcwang0602/MLLMSeg_InternVL2_5_8B_GRES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcwang0602/MLLMSeg_InternVL2_5_8B_GRES with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="jcwang0602/MLLMSeg_InternVL2_5_8B_GRES", trust_remote_code=True)# Load model directly from transformers import MLLMSeg model = MLLMSeg.from_pretrained("jcwang0602/MLLMSeg_InternVL2_5_8B_GRES", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, library name, and paper/code links
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for the MLLMSeg model by:
- Adding the
pipeline_tag: image-segmentation, making the model discoverable on the Hugging Face Hub (e.g., at https://huggingface.co/models?pipeline_tag=image-segmentation). - Specifying
library_name: transformers, allowing for the "Use in Transformers" widget to appear on the model page and indicating library compatibility. - Including a direct link to the paper on Hugging Face Papers.
- Adding a link to the official GitHub repository for easy access to the code and further details.
- Providing a clear abstract, a visual overview of the method, and a comprehensive usage example including installation and a runnable Python code snippet.
- Incorporating performance metrics, checkpoints, and visualization examples directly from the official GitHub repository to give users a complete understanding of the model's capabilities.
- Adding the academic citation and acknowledgments for proper attribution.
Please review and merge this PR.
jcwang0602 changed pull request status to merged