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 pytorch_model.bin from KETI-NLP/veld-base: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/KETI-NLP/veld-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KETI-NLP/veld-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KETI-NLP/veld-base/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 21c06db6c3fe763c75b1d7c28d98082aab38ba14ac8190f858e4a9ca49032e93
- Size of remote file:
- 1.35 GB
- SHA256:
- 1358115538068e93bc8d7aa24bfd3c6bf18946be166e1394ac5a5f716df6dbfe
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