GREEN-RadLlama2-7b – AI Model by StanfordAIMI | AlphaNeural AI

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StanfordAIMI/
GREEN-RadLlama2-7b

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text-generation

generated_from_trainer

StanfordAIMI/RadLLaMA-7b

autotrain_compatible

text-generation-inference

endpoints_compatible

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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