Instructions to use JunxiongWang/BiGS_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_512")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JunxiongWang/BiGS_512: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/JunxiongWang/BiGS_512/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JunxiongWang/BiGS_512/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JunxiongWang/BiGS_512/resolve/main/flax_model.msgpack
1.39 GB
- Xet hash:
- 144553dea83b3142c4965e601c19527b1a655bc64aeaacc6695b9f02ca09e371
- Size of remote file:
- 1.39 GB
- SHA256:
- 6f016920d5f728ed57d3676da203a30b734f36a4e0840258fff3f221753ccbfb
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