Instructions to use jcwang0602/MLLMSeg_InternVL2_5_1B_RES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcwang0602/MLLMSeg_InternVL2_5_1B_RES with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="jcwang0602/MLLMSeg_InternVL2_5_1B_RES", trust_remote_code=True)# Load model directly from transformers import MLLMSeg model = MLLMSeg.from_pretrained("jcwang0602/MLLMSeg_InternVL2_5_1B_RES", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20fd7a60984793a3e17a9832397365bc5fa015dfebcde99197395b57abf50bc9
- Size of remote file:
- 1.93 GB
- SHA256:
- 5407c53865a51c5401c53429d2d396adb7849646bb9bae9e25ace0a8d34e03bd
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