---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
license: llama3.1
language:
- gl
metrics:
- bleu
- rouge
model-index:
- name: Llama-3.1-8B-Instruct-Galician
  results:
  - task:
      type: text-generation
    dataset:
      name: alpaca_data_galician
      type: alpaca_data_galician
    metrics:
    - name: bleu
      type: bleu-4
      value: 23.13
    - name: rouge
      type: rouge-l
      value: 21.84
pipeline_tag: text-generation
library_name: transformers
---

# Llama-3.1-8B-Instruct-Galician

This model is a continued pretraining version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [CorpusNós](https://zenodo.org/records/11655219) dataset.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [UDC Information Retrieval Lab (IRLab)](https://huggingface.co/irlab-udc)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** Multilingual, adapted to Galician
- **License:** llama3.1
- **Finetuned from model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)

### Model Sources

- **Repository:** [Adapting Large Language Models for Underrepresented Languages](https://gitlab.irlab.org/eliseo.bao/xovetic-llms-underrepresented-languages)
- **Paper:** _Coming soon_

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

[More Information Needed]

### Training Data

[More Information Needed]

### Training Procedure

[More Information Needed]

#### Training Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0

#### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0606        | 0.1682 | 900  | 2.0613          |
| 1.9898        | 0.3363 | 1800 | 1.9929          |
| 1.9847        | 0.5045 | 2700 | 1.9613          |
| 1.9577        | 0.6726 | 3600 | 1.9445          |
| 1.9287        | 0.8408 | 4500 | 1.9368          |

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** 4x NVIDIA A100 SXM4 80 GB (TDP of 400W)
- **Hours used:** 60
- **Cloud Provider:** Private infrastructure
- **Carbon Emitted:** 10.37 kgCO$_2$eq

#### Software

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1

## Citation

**BibTeX:**

_Coming soon_