Instructions to use cuong1692001/gemma-3-4b-it_high with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cuong1692001/gemma-3-4b-it_high with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cuong1692001/gemma-3-4b-it_high", dtype="auto") - Notebooks
- Google Colab
- Kaggle
gemma-3-4b-it_high
This model is a fine-tuned version of google/gemma-3-4b-it on the gemma-3-4b-it_high dataset.
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: 1.25e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5.0
Training results
Framework versions
- Transformers 4.56.2
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.22.2
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