|
--- |
|
license: other |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- HiTZ/alpaca_mt |
|
model-index: |
|
- name: alpaca-lora-7b-en-pt-es-ca-eu-gl-at |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# alpaca-lora-7b-en-pt-es-ca-eu-gl-at |
|
|
|
This model is a fine-tuned version of [decapoda-research/llama-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf) on the HiTZ/alpaca_mt ['en', 'pt', 'es', 'ca', 'eu', 'gl', 'at'] dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.0667 |
|
|
|
## 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: 0.0003 |
|
- train_batch_size: 26 |
|
- eval_batch_size: 26 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- gradient_accumulation_steps: 5 |
|
- total_train_batch_size: 130 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.03 |
|
- num_epochs: 1 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 1.3772 | 0.04 | 100 | 1.3860 | |
|
| 1.3043 | 0.07 | 200 | 1.2904 | |
|
| 1.2307 | 0.11 | 300 | 1.2409 | |
|
| 1.2132 | 0.15 | 400 | 1.2086 | |
|
| 1.1987 | 0.19 | 500 | 1.1854 | |
|
| 1.1551 | 0.22 | 600 | 1.1660 | |
|
| 1.1613 | 0.26 | 700 | 1.1516 | |
|
| 1.144 | 0.3 | 800 | 1.1407 | |
|
| 1.1494 | 0.34 | 900 | 1.1297 | |
|
| 1.1072 | 0.37 | 1000 | 1.1196 | |
|
| 1.1302 | 0.41 | 1100 | 1.1117 | |
|
| 1.1074 | 0.45 | 1200 | 1.1058 | |
|
| 1.0846 | 0.48 | 1300 | 1.0995 | |
|
| 1.086 | 0.52 | 1400 | 1.0935 | |
|
| 1.0793 | 0.56 | 1500 | 1.0889 | |
|
| 1.0931 | 0.6 | 1600 | 1.0847 | |
|
| 1.0905 | 0.63 | 1700 | 1.0804 | |
|
| 1.0793 | 0.67 | 1800 | 1.0775 | |
|
| 1.0795 | 0.71 | 1900 | 1.0748 | |
|
| 1.0861 | 0.74 | 2000 | 1.0725 | |
|
| 1.0881 | 0.78 | 2100 | 1.0705 | |
|
| 1.0673 | 0.82 | 2200 | 1.0691 | |
|
| 1.0626 | 0.86 | 2300 | 1.0681 | |
|
| 1.0633 | 0.89 | 2400 | 1.0674 | |
|
| 1.0601 | 0.93 | 2500 | 1.0669 | |
|
| 1.0849 | 0.97 | 2600 | 1.0667 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.0.dev0 |
|
- Pytorch 2.0.0+cu117 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.13.2 |
|
|
