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gemma270-medical-dapt
yasminekaced/gemma270-medical-dapt
gemma270-medical-dapt is a text generation model from yasminekaced. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as gemma.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- gemma
- Model type
- gemma3_text
- Access
- Public
- Created
- Sep 8, 2025
- Updated
- Sep 8, 2025
- SHA
- 6905a0f7
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
gemma270-medical-dapt
This model is a fine-tuned version of google/gemma-3-270m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.9938
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5998 | 0.7582 | 1000 | 2.8980 |
| 2.3657 | 1.5163 | 2000 | 2.8932 |
| 2.1833 | 2.2745 | 3000 | 2.9254 |
| 2.1202 | 3.0326 | 4000 | 2.9899 |
| 2.0536 | 3.7908 | 5000 | 2.9938 |
Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
- Task
- Text Generation
- Library
- transformers
- Type
- gemma3_text
- License
- gemma
- Created
- Sep 8, 2025
- Updated
- Sep 8, 2025