Instructions to use Bingsu/mega-150m-arch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/mega-150m-arch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Bingsu/mega-150m-arch")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Bingsu/mega-150m-arch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Bingsu/mega-150m-arch: direct link, hf CLI and curl.
- Browser
- Download file 452 MB
-
https://huggingface.co/Bingsu/mega-150m-arch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Bingsu/mega-150m-arch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Bingsu/mega-150m-arch/resolve/main/pytorch_model.bin
452 MB
- Xet hash:
- 61fbbbadf7354bbd1460743d1f4695532f5832b657e0584f1366f0fc8d5b4a5b
- Size of remote file:
- 452 MB
- SHA256:
- 0388ab0ab0fe9e2f805cd88ae163317b8a76b9f30864d4e3be491b00d1aaf289
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