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README.md
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---
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library_name: peft
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license: cc-by-nc-4.0
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base_model: facebook/nllb-200-distilled-600M
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tags:
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- generated_from_trainer
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model-index:
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- name: nllb-lora-Paiwan
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# nllb-lora-Paiwan
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This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 7.3958
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.001
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 20
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 7.4934 | 1.0 | 201 | 7.4371 |
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| 7.4808 | 2.0 | 402 | 7.4273 |
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| 7.4608 | 3.0 | 603 | 7.4223 |
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| 7.4547 | 4.0 | 804 | 7.4194 |
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| 7.4386 | 5.0 | 1005 | 7.4172 |
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| 7.4414 | 6.0 | 1206 | 7.4116 |
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| 7.4349 | 7.0 | 1407 | 7.4079 |
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| 7.4189 | 8.0 | 1608 | 7.4040 |
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| 7.4228 | 9.0 | 1809 | 7.4029 |
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| 7.4111 | 10.0 | 2010 | 7.4001 |
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| 7.4093 | 11.0 | 2211 | 7.3995 |
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| 7.4088 | 12.0 | 2412 | 7.3982 |
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| 7.4067 | 13.0 | 2613 | 7.3981 |
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| 7.402 | 14.0 | 2814 | 7.3971 |
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| 7.3989 | 15.0 | 3015 | 7.3963 |
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| 7.3954 | 16.0 | 3216 | 7.3960 |
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| 7.3914 | 17.0 | 3417 | 7.3960 |
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| 7.3803 | 18.0 | 3618 | 7.3959 |
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| 7.3742 | 19.0 | 3819 | 7.3963 |
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| 7.3832 | 20.0 | 4020 | 7.3958 |
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### Framework versions
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- PEFT 0.15.0
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- Transformers 4.51.2
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- Pytorch 2.2.2+cu118
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- Datasets 3.5.0
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- Tokenizers 0.21.1
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