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# alpaca-lora-13b-en-pt-es-ca-eu-gl-at

This model is a fine-tuned version of [decapoda-research/llama-13b-hf](https://huggingface.co/decapoda-research/llama-13b-hf) on the HiTZ/alpaca_mt ['en', 'pt', 'es', 'ca', 'eu', 'gl', 'at'] dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9967

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.303         | 0.04  | 100  | 1.2875          |
| 1.2153        | 0.07  | 200  | 1.2016          |
| 1.1584        | 0.11  | 300  | 1.1560          |
| 1.1426        | 0.15  | 400  | 1.1277          |
| 1.1198        | 0.18  | 500  | 1.1063          |
| 1.0631        | 0.22  | 600  | 1.0911          |
| 1.0714        | 0.26  | 700  | 1.0773          |
| 1.0505        | 0.29  | 800  | 1.0667          |
| 1.0475        | 0.33  | 900  | 1.0562          |
| 1.0411        | 0.37  | 1000 | 1.0485          |
| 1.0418        | 0.4   | 1100 | 1.0413          |
| 1.0419        | 0.44  | 1200 | 1.0339          |
| 1.0315        | 0.48  | 1300 | 1.0290          |
| 1.0235        | 0.51  | 1400 | 1.0238          |
| 1.0308        | 0.55  | 1500 | 1.0189          |
| 1.0039        | 0.59  | 1600 | 1.0157          |
| 1.0048        | 0.62  | 1700 | 1.0110          |
| 0.9982        | 0.66  | 1800 | 1.0080          |
| 1.0196        | 0.7   | 1900 | 1.0049          |
| 1.019         | 0.73  | 2000 | 1.0030          |
| 1.0037        | 0.77  | 2100 | 1.0009          |
| 1.0003        | 0.81  | 2200 | 0.9995          |
| 0.9942        | 0.84  | 2300 | 0.9982          |
| 0.9986        | 0.88  | 2400 | 0.9974          |
| 0.9987        | 0.92  | 2500 | 0.9969          |
| 0.9763        | 0.95  | 2600 | 0.9967          |
| 0.9733        | 0.99  | 2700 | 0.9967          |


### 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-13b-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-13b-en-pt-es-ca-eu-gl-at
====================================


This model is a fine-tuned version of decapoda-research/llama-13b-hf on the HiTZ/alpaca\_mt ['en', 'pt', 'es', 'ca', 'eu', 'gl', 'at'] dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9967


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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* 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