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README.md
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---
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license: other
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tags:
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- generated_from_trainer
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model-index:
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- name: alpaca-lora-30b-en-pt-es-ca-eu-gl-at
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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-30b-en-pt-es-ca-eu-gl-at
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This model is a fine-tuned version of [decapoda-research/llama-30b-hf](https://huggingface.co/decapoda-research/llama-30b-hf) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9088
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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: 6
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- eval_batch_size: 6
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- seed: 42
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- distributed_type: multi-GPU
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- gradient_accumulation_steps: 21
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- total_train_batch_size: 126
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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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| 1.1695 | 0.04 | 100 | 1.1716 |
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| 1.1211 | 0.07 | 200 | 1.0964 |
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| 1.0591 | 0.11 | 300 | 1.0590 |
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| 1.0234 | 0.14 | 400 | 1.0341 |
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| 1.0345 | 0.18 | 500 | 1.0165 |
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| 0.9932 | 0.22 | 600 | 1.0024 |
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| 0.9948 | 0.25 | 700 | 0.9895 |
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| 1.01 | 0.29 | 800 | 0.9794 |
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| 0.9488 | 0.32 | 900 | 0.9708 |
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| 0.9518 | 0.36 | 1000 | 0.9627 |
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| 0.9463 | 0.4 | 1100 | 0.9557 |
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| 0.956 | 0.43 | 1200 | 0.9498 |
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| 0.9521 | 0.47 | 1300 | 0.9437 |
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| 0.9345 | 0.51 | 1400 | 0.9385 |
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| 0.9469 | 0.54 | 1500 | 0.9337 |
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| 0.9466 | 0.58 | 1600 | 0.9297 |
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| 0.9403 | 0.61 | 1700 | 0.9257 |
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| 0.9179 | 0.65 | 1800 | 0.9219 |
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| 0.9468 | 0.69 | 1900 | 0.9190 |
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| 0.9173 | 0.72 | 2000 | 0.9163 |
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| 0.9172 | 0.76 | 2100 | 0.9142 |
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| 0.9351 | 0.79 | 2200 | 0.9124 |
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| 0.9238 | 0.83 | 2300 | 0.9110 |
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| 0.9057 | 0.87 | 2400 | 0.9099 |
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| 0.9309 | 0.9 | 2500 | 0.9093 |
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| 0.8893 | 0.94 | 2600 | 0.9090 |
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| 0.9095 | 0.97 | 2700 | 0.9088 |
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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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