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:
- 29662c51e335963e69db82ac31bf84ab7998161a4cbb362b5830be368b2750d3
- Size of remote file:
- 2.12 MB
- SHA256:
- 672adaed43fbaccd49e229752e5bd101bc5584b9ac0730b59a8142d092fded8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.