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---
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
widget:
- text: "Onde está o concello de Frades?"
output:
text: Frades é un concello da provincia da Coruña, pertencente á comarca de Ordes. Está situado a 15 quilómetros de Santiago de Compostela.
---
# 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 Description
- **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)
- **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
```python
import transformers
import torch
model_id = "irlab-udc/Llama-3.1-8B-Instruct-Galician"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a conversational AI that always responds in Galician."},
{"role": "user", "content": "Cal é a principal vantaxe de usar Scrum?"},
]
outputs = pipeline(messages, max_new_tokens=512)
print(outputs[0]["generated_text"][-1]["content"])
```
[More Information Needed]
## Training Details
[More Information Needed]
### Training Data
[More Information Needed]
#### Training Hyperparameters
| Parameter | Value |
|--------------------------------|--------------------------------------|
| 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), 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 Kg. CO₂ eq.
## Citation
_Coming soon_ |