proxectonos/Llama-Carvalho-PT-GL_300k
This repository contains proxectonos/Llama-Carvalho-PT-GL_300k, a LoRA adapter for machine translation from Spanish to Galician, built on top of Nos-PT/Llama-Carvalho-PT-GL.
Why this model exists
This checkpoint is the 300k training-size model from our data-scaling experiments on low-resource machine translation with LLM fine-tuning. It is being published because it was the best-performing model for the es-gl direction among the four training sizes we evaluated: 30k, 300k, 600k, and 3.6M parallel sentence pairs.
The motivation for releasing this model is not just to provide another translation adapter, but to document a central experimental result: for Spanish-to-Galician, a closely related language pair, the best translation quality was obtained with a medium-sized corpus rather than with the largest available one. In our experiments, the 300k model achieved the strongest overall results for es-gl, especially in BLEU and human evaluation.
This makes the model an important counterpart to the 3.6M English-Galician checkpoint from the same study. Together, these releases illustrate the main conclusion of the work: the optimal fine-tuning size depends on linguistic distance, and more data is not always better for closely related language pairs.
Model details
- Base model:
Nos-PT/Llama-Carvalho-PT-GL
- Model type: LoRA adapter for causal language modeling used as machine translation
- Task: Machine translation
- Direction: Spanish -> Galician
- Languages: Spanish, Galician
- Training method: Supervised Fine-Tuning with LoRA
- Training size: 300,000 parallel sentence pairs
Training data
The adapter was trained on 300k Spanish-Galician parallel sentence pairs.
The training data comes from the CorpusNos MT dataset and includes only human-written text. The corpus covers several text types, including:
- formal institutional language
- scientific text
- spoken-domain material
- multi-word expressions
Training setup
All experiments in the paper used the same base model and the same fine-tuning setup so that differences in quality could be attributed to data scale rather than to architecture changes.
- Base model:
Llama-Carvalho-PT-GL
- Fine-tuning strategy: LoRA
- Learning rate:
5e-6
- Scheduler: cosine
- Warmup steps:
150
- Weight decay:
0.01
- Per-device train batch size:
16
- Gradient accumulation steps:
4
- Precision:
bf16
Evaluation summary
This model was evaluated in the paper against other checkpoints trained on smaller and larger Spanish-Galician corpora (30k, 600k, 3.6M).
Among the es-gl models, this 300k checkpoint obtained the best overall results:
- Average BLEU across 6 benchmark test sets:
60.63
- Average COMET across 6 benchmark test sets:
0.9076
- Human evaluation on 74 test-suite sentences:
53.67% correct
The 6 benchmark test sets used to compute the average BLEU and COMET values were:
gold1gold2test-suiteflorestatoebataCoN
The first 3 datasets are our own, you can find the links
here.
Human evaluation was carried out by 3 native Galician annotators using binary correct/incorrect judgments.
These results support the main finding for es-gl: unlike more distant language pairs, Spanish-Galician reaches its best quality at an intermediate training size, while larger corpora can introduce source-language interference.
Intended use
This model is intended for:
- research on Spanish-Galician machine translation
- evaluation of data scaling in LLM fine-tuning
- practical translation workflows into Galician when using the Carvalho model family
Limitations
- This is a LoRA adapter, not a standalone merged model.
- It is specialized for Spanish-to-Galician translation and should not be treated as a general-purpose chat assistant.
- The model inherits the capabilities and limitations of the base model.
- As reported in the paper, translation quality depends strongly on language pair and corpus design; the best training size for
es-gl should not be assumed to be optimal for other directions.
License
MIT license
Funding and acknowledgements
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA.
