Instructions to use JunxiongWang/BiGS_1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_1024")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JunxiongWang/BiGS_1024: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/JunxiongWang/BiGS_1024/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JunxiongWang/BiGS_1024/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JunxiongWang/BiGS_1024/resolve/main/flax_model.msgpack
1.39 GB
- Xet hash:
- a2876ccb02d60197eddaba57cb160a9008c4abd13de5ea6f935242158c624806
- Size of remote file:
- 1.39 GB
- SHA256:
- 6b2d632945167eff28baead8ea0fbbf285be03dce9bc92ca4686e27b7903184f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.