Instructions to use ljt019/delta-embed-vl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljt019/delta-embed-vl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ljt019/delta-embed-vl")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ljt019/delta-embed-vl") model = AutoModel.from_pretrained("ljt019/delta-embed-vl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ljt019/delta-embed-vl: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/ljt019/delta-embed-vl/resolve/main/tokenizer.json
- Command line
-
hf download hf://ljt019/delta-embed-vl/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ljt019/delta-embed-vl/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 05c31fa6ca22ab5dab08810c349d3108a9fa3cbf987ac1baeba17ec6052cec11
- Size of remote file:
- 20 MB
- SHA256:
- 52df673999cebab9026abe23a838ada092cc0cf3aa9e9aefb9d27f3b3b69927e
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