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# 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

{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["HiTZ/alpaca_mt"], "model-index": [{"name": "alpaca-lora-7b-en-pt-es-ca-eu-gl-at", "results": []}]}
generated_from_trainer dataset:HiTZ/alpaca_mt license:other region:us
TAGS
#generated_from_trainer #dataset-HiTZ/alpaca_mt #license-other #region-us 

alpaca-lora-7b-en-pt-es-ca-eu-gl-at
===================================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#generated_from_trainer #dataset-HiTZ/alpaca_mt #license-other #region-us 
 ### 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 ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#generated_from_trainer #dataset-HiTZ/alpaca_mt #license-other #region-us 
### 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### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2