Instructions to use unc-nlp/lxmert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unc-nlp/lxmert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="unc-nlp/lxmert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("unc-nlp/lxmert-base-uncased") model = AutoModel.from_pretrained("unc-nlp/lxmert-base-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from unc-nlp/lxmert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 1.01 GB
-
https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://unc-nlp/lxmert-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/pytorch_model.bin
1.01 GB
- Xet hash:
- e309decccf5cb17d468de351af4ea87c82d7d777aa0ab9a48250161b4ca5d63a
- Size of remote file:
- 1.01 GB
- SHA256:
- 9bbe6ff6d63efac02517dac29c9175e853d1825580e2f1d31c0dee2e4d014de1
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