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--- |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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license: llama3.1 |
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language: |
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- gl |
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metrics: |
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- bleu |
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- rouge |
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model-index: |
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- name: Llama-3.1-8B-Instruct-Galician |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: alpaca_data_galician |
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type: alpaca_data_galician |
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metrics: |
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- name: bleu |
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type: bleu-4 |
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value: 23.13 |
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- name: rouge |
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type: rouge-l |
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value: 21.84 |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# Llama-3.1-8B-Instruct-Galician |
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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. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [UDC Information Retrieval Lab (IRLab)](https://huggingface.co/irlab-udc) |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** Multilingual, adapted to Galician |
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- **License:** llama3.1 |
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- **Finetuned from model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |
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### Model Sources |
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- **Repository:** [Adapting Large Language Models for Underrepresented Languages](https://gitlab.irlab.org/eliseo.bao/xovetic-llms-underrepresented-languages) |
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- **Paper:** _Coming soon_ |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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[More Information Needed] |
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### Training Data |
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[More Information Needed] |
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### Training Procedure |
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[More Information Needed] |
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#### Training Hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1.0 |
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#### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.0606 | 0.1682 | 900 | 2.0613 | |
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| 1.9898 | 0.3363 | 1800 | 1.9929 | |
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| 1.9847 | 0.5045 | 2700 | 1.9613 | |
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| 1.9577 | 0.6726 | 3600 | 1.9445 | |
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| 1.9287 | 0.8408 | 4500 | 1.9368 | |
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## Environmental Impact |
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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). |
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- **Hardware Type:** 4x NVIDIA A100 SXM4 80 GB (TDP of 400W) |
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- **Hours used:** 60 |
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- **Cloud Provider:** Private infrastructure |
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- **Carbon Emitted:** 10.37 kgCO$_2$eq |
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#### Software |
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- PEFT 0.12.0 |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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## Citation |
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**BibTeX:** |
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_Coming soon_ |
