File size: 2,995 Bytes
ac57142 2235636 f743fbb ac57142 2a8e940 ac57142 2a8e940 ac57142 2a8e940 ac57142 2a8e940 ac57142 2a8e940 ac57142 97ff87f ac57142 97ff87f ac57142 2a8e940 ac57142 97ff87f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
---
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_ |