StanfordAIMI/GREEN This model is a fine tuned version of StanfordAIMI/RadLLaMA 7b. It achieves the following results on the evaluation set: Loss: 0.0644 Model description and Training procedure Please see the project website at https://stanford aimi.github.io/green.html. Intended uses & limitations This model is finetuned to evaluate the difference between the reference and candidate radiology report for Chest Xrays. Training hyperparameters The following hyperparameters were used during training: learning rate: 0.0001 train batch size: 8 eval batch size: 8 seed: 42 distributed type: multi GPU num devices: 8 gradient accumulation steps: 32 total train batch size: 2048 total eval batch size: 64 optimizer: Adam with betas=(0.9,0.95) and epsilon=1e 08 lr scheduler type: cosine lr scheduler warmup ratio: 0.05 num epochs: 3.0 Training results Training Loss Epoch Step Validation Loss : : : : : : : : 0.2634 0.64 25 0.2924 0.1216 1.28 50 0.0898 0.0833 1.92 75 0.0718 0.062 2.56 100 0.0644 Framework versions Transformers 4.38.0.dev0 Pytorch 2.2.0+cu121 Datasets 2.16.1 Tokenizers 0.15.1
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