proxectonos/Llama-3.1-Carballo-Instr3

Carballo-Llama-Instr3

Table of Contents

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• Carballo-Llama-Instr3
  • Table of Contents
  • Model description
  • Intended uses and limitations
  • How to use
  • Training
    • Tools
    • Training data
    • Training hyperparameters
    • Framework
  • Evaluation
  • Additional information
    • Contact
    • License
    • Funding

Model description

Carballo-Llama-Instr3 (or Llama-3.1-Carballo-Instr3) is a 8B-parameter transformer-based causal language model for Galician, Portuguese, Spanish, English and Catlan. It is the result of a continual pretraining of meta-llama/Llama-3.1-8B with a multilingual corpus of 340M tokens with emphasis in Galician.

This model is part of the experiments associated with the paper Continued Pretraining and Interpretability-Based Evaluation for Low-Resource Languages: A Galician Case Study, accepted in the 2025 ACL Findings.

Intended uses and limitations

The Carballo-Llama-Instr3 model is ready-to-use only for causal language modeling. It can perform text-generation tasks and be fine-tuned for specific scenarios.

How to use

import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

input_text = "Hoxe fai un bo día. O sol  "

model_id  = "proxectonos/Llama-3.1-Carballo-Instr3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
generation = generator(
    input_text,
    do_sample=True,
    top_k=10,
    eos_token_id=tokenizer.eos_token_id
)

print(f"Result: {generation[0]['generated_text']}")

Training

Tools

It was trained using HuggingFace Transformers and Pytorch, using the Causal Modeling Language script. We also use DeepSpeed to deal with the huge size of the model.

Training data

The training corpus consists of texts in 4 languages, with an emphasis on Galician. The main aim of this is to ensure that the model learns to work with this language perfectly, while maintaining knowledge of languages already known (Spanish, English), learning others (Galician) or adapting existing language varieties (Portuguese-PT instead of Portuguese-BR).

The corpus is composed as follows:

Corpus		gl	pt	es	en	cat
Base plain text corpus	Tokens	232M	29M	29M	29M	29M
	Percentage (of the total base corpus)	74%	8.3%	8.3%	8.3%	8.3%
Instructions	30M Tokens (multilingual)

Training hyperparameters

• seed: 42
• num_devices: 1
• train_batch_size: 4
• eval_batch_size: 4
• gradient_acummulation: 4
• optimizer: AdamW
• betas: (0.9,0.999)
• epsilon: 1e-08
• weight_decay_rate: 0.1
• scheduler: "Linear"
• learning_rate: 1e-04
• num_epochs: 1.0

Framework

The training was conducted on the Galician Supercomputing Center (CESGA), using 4 nodes with 2 GPUs NVIDIA A100 40G.

Evaluation

In process...

Additional information

Contact

For further information, please send an email to

License

MIT License

Copyright (c) 2025

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Funding

This model was development within the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215336.

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