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# alpaca-lora-65b-en-pt-es-ca

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.
It achieves the following results on the evaluation set:
- Loss: 0.7271

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 63
- total_train_batch_size: 126
- total_eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8069        | 0.06  | 100  | 0.8033          |
| 0.8008        | 0.13  | 200  | 0.7826          |
| 0.7687        | 0.19  | 300  | 0.7721          |
| 0.7719        | 0.25  | 400  | 0.7647          |
| 0.7585        | 0.32  | 500  | 0.7588          |
| 0.7578        | 0.38  | 600  | 0.7537          |
| 0.7505        | 0.44  | 700  | 0.7491          |
| 0.7531        | 0.51  | 800  | 0.7449          |
| 0.7394        | 0.57  | 900  | 0.7416          |
| 0.7368        | 0.63  | 1000 | 0.7387          |
| 0.7412        | 0.69  | 1100 | 0.7361          |
| 0.7344        | 0.76  | 1200 | 0.7288          |
| 0.7383        | 0.82  | 1300 | 0.7281          |
| 0.7378        | 0.88  | 1400 | 0.7274          |
| 0.7204        | 0.95  | 1500 | 0.7271          |


### Framework versions

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

{"tags": ["generated_from_trainer"], "datasets": ["HiTZ/alpaca_mt"], "model-index": [{"name": "alpaca-lora-65b-en-pt-es-ca", "results": []}]}
pytorch generated_from_trainer dataset:HiTZ/alpaca_mt has_space region:us
TAGS
#pytorch #generated_from_trainer #dataset-HiTZ/alpaca_mt #has_space #region-us 

alpaca-lora-65b-en-pt-es-ca
===========================


This model is a fine-tuned version of /gaueko1/hizkuntza-ereduak/LLaMA/lm/huggingface/65B on the HiTZ/alpaca\_mt ['en', 'pt', 'es', 'ca'] dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7271


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: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 63
* total\_train\_batch\_size: 126
* total\_eval\_batch\_size: 2
* 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: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 63
* total\_train\_batch\_size: 126
* total\_eval\_batch\_size: 2
* 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
#pytorch #generated_from_trainer #dataset-HiTZ/alpaca_mt #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 63
* total\_train\_batch\_size: 126
* total\_eval\_batch\_size: 2
* 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
#pytorch #generated_from_trainer #dataset-HiTZ/alpaca_mt #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 63
* total\_train\_batch\_size: 126
* total\_eval\_batch\_size: 2
* 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