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
| { | |
| "</box>": 151654, | |
| "</img>": 151647, | |
| "</quad>": 151650, | |
| "</ref>": 151652, | |
| "<IMG_CONTEXT>": 151648, | |
| "<box>": 151653, | |
| "<img>": 151646, | |
| "<quad>": 151649, | |
| "<ref>": 151651, | |
| "<|endoftext|>": 151643, | |
| "<|im_end|>": 151645, | |
| "<|im_start|>": 151644 | |
| } | |