Instructions to use cjfcsjt/internvl15_2000_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cjfcsjt/internvl15_2000_ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cjfcsjt/internvl15_2000_", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cjfcsjt/internvl15_2000_", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92f6c40aa503e8d12763713dc0a754249903a36282202f82634c9a1e6e6360c4
- Size of remote file:
- 6.52 kB
- SHA256:
- ff906a9fb7920e6e8a3a6a493b682cf9bb1ed527f84fdd85ac33d33f1782c2c8
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