Instructions to use jcwang0602/MLLMSeg_InternVL2_5_4B_RES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcwang0602/MLLMSeg_InternVL2_5_4B_RES with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="jcwang0602/MLLMSeg_InternVL2_5_4B_RES", trust_remote_code=True)# Load model directly from transformers import MLLMSeg model = MLLMSeg.from_pretrained("jcwang0602/MLLMSeg_InternVL2_5_4B_RES", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae288514c0a49807ea5c39fdc54e3adf055cd95b488bffd59d383741e3150fb5
- Size of remote file:
- 2.49 GB
- SHA256:
- 5f9b8bb69ec0b29225113a53a4517e06085562b3aac1bfacfb47efd405a71830
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