Instructions to use aayush7511/Gemma3_12B_PT_Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aayush7511/Gemma3_12B_PT_Checkpoints with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/gemma-3-12b-pt") model = PeftModel.from_pretrained(base_model, "aayush7511/Gemma3_12B_PT_Checkpoints") - Transformers
How to use aayush7511/Gemma3_12B_PT_Checkpoints with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aayush7511/Gemma3_12B_PT_Checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Gemma3_12B_PT_Checkpoints
This model is a fine-tuned version of google/gemma-3-12b-pt on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1984
- Accuracy: 0.796
- Precision: 0.582
- Recall: 0.813
- Auc: 0.847
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: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Auc |
|---|---|---|---|---|---|---|---|
| 0.2849 | 1.0 | 73 | 0.3395 | 0.51 | 0.347 | 0.964 | 0.759 |
| 0.2075 | 2.0 | 146 | 0.2094 | 0.724 | 0.489 | 0.908 | 0.826 |
| 0.1453 | 3.0 | 219 | 0.1984 | 0.796 | 0.582 | 0.813 | 0.847 |
Framework versions
- PEFT 0.18.0
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1
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Base model
google/gemma-3-12b-pt