Instructions to use TSunm/InternVL2-1B-ViVQA-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TSunm/InternVL2-1B-ViVQA-X with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TSunm/InternVL2-1B-ViVQA-X", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TSunm/InternVL2-1B-ViVQA-X", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 292496c1c28552a4f7e1d1818452806bdad05687dfb2f1d6cfba850701a35309
- Size of remote file:
- 8.79 kB
- SHA256:
- 41721a7ab2d94e701b36cc22a2638022c398becc2ac8d17016e5e5e94680517b
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