Instructions to use aehrc/cxrmate-rrg24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/cxrmate-rrg24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aehrc/cxrmate-rrg24", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True) model = AutoModel.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -39,8 +39,8 @@ from torch.utils.data import DataLoader
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mbatch_size = 1
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device = 'cuda'
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tokenizer = transformers.AutoTokenizer.from_pretrained('aehrc/cxrmate-rrg24')
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model = transformers.AutoModel.from_pretrained('aehrc/cxrmate-rrg24', trust_remote_code=True)
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transforms = v2.Compose(
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[
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v2.PILToTensor(),
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mbatch_size = 1
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device = 'cuda'
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tokenizer = transformers.AutoTokenizer.from_pretrained('aehrc/cxrmate-rrg24')
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model = transformers.AutoModel.from_pretrained('aehrc/cxrmate-rrg24', trust_remote_code=True).to(device=device)
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transforms = v2.Compose(
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[
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v2.PILToTensor(),
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