Instructions to use harish/PT-v3-dev-test-all-PreTrain-e5-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/PT-v3-dev-test-all-PreTrain-e5-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="harish/PT-v3-dev-test-all-PreTrain-e5-all")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("harish/PT-v3-dev-test-all-PreTrain-e5-all") model = AutoModelForMaskedLM.from_pretrained("harish/PT-v3-dev-test-all-PreTrain-e5-all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from harish/PT-v3-dev-test-all-PreTrain-e5-all: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e5-all/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://harish/PT-v3-dev-test-all-PreTrain-e5-all/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e5-all/resolve/main/flax_model.msgpack
712 MB
- Xet hash:
- 091cf76645ee28e7944449bb96b30ac28fee9a182a383f2bacdf77feb652d1d7
- Size of remote file:
- 712 MB
- SHA256:
- 5270bd68b11be51d8954fed2ef5a3b65e6f8bd5ba110e88a4ff0b8bede80ab6d
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