Visual Question Answering
Transformers
Safetensors
Vietnamese
English
internvl_chat
image-feature-extraction
vision
custom_code
Instructions to use tt1225/Vintern-1B-v2-Custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tt1225/Vintern-1B-v2-Custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="tt1225/Vintern-1B-v2-Custom", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tt1225/Vintern-1B-v2-Custom", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68d2a51420ccb8663b8eb7157d2e76e9ac60fdfca6e3eca37ea846ddc1f11a6f
- Size of remote file:
- 1.88 GB
- SHA256:
- c95ece988fd66141dd22d5cb5d4651d067fea339a2725ef5ac9337b20ca6e4d4
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