Instructions to use JunxiongWang/BiGS_4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_4096 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_4096")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_4096", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JunxiongWang/BiGS_4096: direct link, hf CLI and curl.
- Browser
- Download file 1.4 GB
-
https://huggingface.co/JunxiongWang/BiGS_4096/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JunxiongWang/BiGS_4096/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JunxiongWang/BiGS_4096/resolve/main/flax_model.msgpack
1.4 GB
- Xet hash:
- 2c3ee9e13a5ce373e28713e48788472435edd59eadb6e016f9d36624d21813c4
- Size of remote file:
- 1.4 GB
- SHA256:
- 4692be78d8cca6b3539319361f7a3e1aec0979ba56081ee98b6c9876f170f095
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