Instructions to use aehrc/cxrmate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/cxrmate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aehrc/cxrmate", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aehrc/cxrmate", trust_remote_code=True) model = AutoModel.from_pretrained("aehrc/cxrmate", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update modelling_longitudinal.py
Browse filesFixed issues with attn_implementation and decoder_inputs['past_key_values'].
modelling_longitudinal.py
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@@ -127,6 +127,7 @@ class LongitudinalPromptMultiCXREncoderDecoderModel(VisionEncoderDecoderModel):
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encoder = MultiCvtWithProjectionHead(config=config.encoder)
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# Decoder:
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if decoder is None:
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decoder = transformers.BertLMHeadModel(config=config.decoder)
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encoder = MultiCvtWithProjectionHead(config=config.encoder)
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# Decoder:
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config.decoder._attn_implementation = 'eager'
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if decoder is None:
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decoder = transformers.BertLMHeadModel(config=config.decoder)
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