Instructions to use hamzabenX/marian-lora-darija with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hamzabenX/marian-lora-darija with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-ar") model = PeftModel.from_pretrained(base_model, "hamzabenX/marian-lora-darija") - Transformers
How to use hamzabenX/marian-lora-darija with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hamzabenX/marian-lora-darija", dtype="auto") - Notebooks
- Google Colab
- Kaggle
marian-lora-darija
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on an unknown 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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
Framework versions
- PEFT 0.16.0
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
- Downloads last month
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Model tree for hamzabenX/marian-lora-darija
Base model
Helsinki-NLP/opus-mt-en-ar