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 generation_config.json from KETI-NLP/veld-base: direct link, hf CLI and curl.
- Browser
- Download file 142 Bytes
-
https://huggingface.co/KETI-NLP/veld-base/resolve/main/generation_config.json
- Command line
-
hf download hf://KETI-NLP/veld-base/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/KETI-NLP/veld-base/resolve/main/generation_config.json
142 Bytes
| { | |
| "_from_model_config": true, | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "pad_token_id": 0, | |
| "transformers_version": "4.26.0" | |
| } | |