Instructions to use harish/PT-v3-dev-test-all-PreTrain-e6-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-e6-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-e6-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-e6-all") model = AutoModelForMaskedLM.from_pretrained("harish/PT-v3-dev-test-all-PreTrain-e6-all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from harish/PT-v3-dev-test-all-PreTrain-e6-all: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e6-all/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://harish/PT-v3-dev-test-all-PreTrain-e6-all/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/harish/PT-v3-dev-test-all-PreTrain-e6-all/resolve/main/flax_model.msgpack
712 MB
- Xet hash:
- 571ecf27552efceff8b548ed12258c62684db14d352e4b44ca8e2ea21f9adfdb
- Size of remote file:
- 712 MB
- SHA256:
- a05fba97d73e50f29acf5749e46818889bdf1623f8148103e9d620c4635be4d0
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