Instructions to use JunxiongWang/BiGS_128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_128")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JunxiongWang/BiGS_128: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/JunxiongWang/BiGS_128/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JunxiongWang/BiGS_128/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JunxiongWang/BiGS_128/resolve/main/flax_model.msgpack
1.39 GB
- Xet hash:
- d9bb6548dbf81a59694e45348600bdfa3aac004523fc0c8d7e4bc76dc54d840f
- Size of remote file:
- 1.39 GB
- SHA256:
- 814c911d575ae509ddd8a3c4cf0ef49b84428ead91d251a0126bb6998270d980
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