Image-to-Text
Transformers
PyTorch
English
Korean
multilingual
veld
feature-extraction
vision, language
pretrained model
custom_code
Instructions to use KETI-NLP/veld-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KETI-NLP/veld-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="KETI-NLP/veld-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KETI-NLP/veld-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from KETI-NLP/veld-base: direct link, hf CLI and curl.
- Browser
- Download file 1.59 MB
-
https://huggingface.co/KETI-NLP/veld-base/resolve/main/spiece.model
- Command line
-
hf download hf://KETI-NLP/veld-base/spiece.model
-
curl -L -o spiece.model https://huggingface.co/KETI-NLP/veld-base/resolve/main/spiece.model
1.59 MB
- Xet hash:
- f1e67045bb723b864b1ca19e43ac5af007726017d3194b6efce36fa4dc3e8a51
- Size of remote file:
- 1.59 MB
- SHA256:
- 4368e6b7901e8c54bd62326ca5a5063eba36c31d74995a599a5be77a0cd5cfd0
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