Instructions to use Salesforce/grappa_large_jnt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/grappa_large_jnt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Salesforce/grappa_large_jnt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/grappa_large_jnt") model = AutoModelForMaskedLM.from_pretrained("Salesforce/grappa_large_jnt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Salesforce/grappa_large_jnt: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/Salesforce/grappa_large_jnt/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Salesforce/grappa_large_jnt/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Salesforce/grappa_large_jnt/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- 5b883572c11fcf112bd31993d1799a47785eeea043de121ae304dc246248b127
- Size of remote file:
- 1.42 GB
- SHA256:
- adf4774f4d86359eeaaee839c204172b1e20e4299d84ef0c24b565080e7443fd
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