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:
- 5c5411ba9c895ae222517056a51b924a5469355da44906b50b3506a604fdcfb0
- Size of remote file:
- 4.99 GB
- SHA256:
- beb924ebc130203171409d2df30efebe326aaf0ef9cd601bc8c0d8a3fddf3775
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