Instructions to use ashabrawy/gumar_aligned_msa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashabrawy/gumar_aligned_msa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ashabrawy/gumar_aligned_msa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ashabrawy/gumar_aligned_msa") model = AutoModel.from_pretrained("ashabrawy/gumar_aligned_msa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ashabrawy/gumar_aligned_msa: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/ashabrawy/gumar_aligned_msa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ashabrawy/gumar_aligned_msa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ashabrawy/gumar_aligned_msa/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- d3c3dcb3fb79cc6844455984ec22fc5f8d47aa209de98fcd819fe73b4965e883
- Size of remote file:
- 436 MB
- SHA256:
- 6cc41997fe1eecd043757a4c8de62cb495fbe10dcdee505fc013a0f9d3e099f3
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