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