Instructions to use SalomonMetre13/nllb_lora_fra_shr_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SalomonMetre13/nllb_lora_fra_shr_v3 with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") model = PeftModel.from_pretrained(base_model, "SalomonMetre13/nllb_lora_fra_shr_v3") - Notebooks
- Google Colab
- Kaggle
nllb_lora_fra_shr_v3
This model is a fine-tuned version of facebook/nllb-200-distilled-600M 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 1
- mixed_precision_training: Native AMP
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Downloads last month
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Base model
facebook/nllb-200-distilled-600M