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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- HiTZ/alpaca_mt |
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model-index: |
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- name: alpaca-lora-65b-en-pt-es-ca |
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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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# alpaca-lora-65b-en-pt-es-ca |
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This model is a fine-tuned version of [/gaueko1/hizkuntza-ereduak/LLaMA/lm/huggingface/65B](https://huggingface.co//gaueko1/hizkuntza-ereduak/LLaMA/lm/huggingface/65B) on the HiTZ/alpaca_mt ['en', 'pt', 'es', 'ca'] dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7271 |
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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: 0.0003 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 63 |
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- total_train_batch_size: 126 |
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- total_eval_batch_size: 2 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 1 |
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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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| 0.8069 | 0.06 | 100 | 0.8033 | |
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| 0.8008 | 0.13 | 200 | 0.7826 | |
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| 0.7687 | 0.19 | 300 | 0.7721 | |
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| 0.7719 | 0.25 | 400 | 0.7647 | |
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| 0.7585 | 0.32 | 500 | 0.7588 | |
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| 0.7578 | 0.38 | 600 | 0.7537 | |
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| 0.7505 | 0.44 | 700 | 0.7491 | |
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| 0.7531 | 0.51 | 800 | 0.7449 | |
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| 0.7394 | 0.57 | 900 | 0.7416 | |
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| 0.7368 | 0.63 | 1000 | 0.7387 | |
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| 0.7412 | 0.69 | 1100 | 0.7361 | |
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| 0.7344 | 0.76 | 1200 | 0.7288 | |
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| 0.7383 | 0.82 | 1300 | 0.7281 | |
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| 0.7378 | 0.88 | 1400 | 0.7274 | |
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| 0.7204 | 0.95 | 1500 | 0.7271 | |
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### Framework versions |
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- Transformers 4.28.0.dev0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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