proxectonos_-_Llama-3.1-Carballo-8bits

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Llama-3.1-Carballo - bnb 8bits

• Model creator: https://huggingface.co/proxectonos/
• Original model: https://huggingface.co/proxectonos/Llama-3.1-Carballo/

Original model description:

language:

• gl
• es
• en
• cat
• pt licence:
• MIT tags:
• galician
• Llama license: llama3.1 inference: parameters: top_k: 10 do_sample: true temperature: 0.4 widget:

• text: |- Traduce ao galego esta frase en inglés: Inglés: "my sister is studying Biology at the university." Galego: "a miña irmá está a estudar bioloxía na universidade."

  Traduce ao galego esta frase en inglés: Inglés: "You are working with my mother on a very interesting project." Galego: "Estás a traballar coa miña nai nun proxecto moi interesante"

  Traduce ao galego esta frase en inglés: Inglés: "You have to fix the computer now" Galego: example_title: Translation

• text: |- Responde á seguinte pregunta. Pregunta: "Cal é a capital de Noruega?" Resposta: "A capital de Noruega é Oslo."

  Responde á seguinte pregunta. Pregunta: "Cal é a moeda de Portugal" Resposta: "A moeda de Portugal é o euro."

  Responde á seguinte pregunta. Pregunta: "Cal é a capital de Suecia?" Resposta: example_title: Question&Answering

• text: |- Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "Estou moi feliz" Polaridade: Positivo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "Non me gusta beber cervexa" Polaridade: Negativo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "O meu pai detesta o seu traballo" Polaridade: Negativo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "Uxía desfruta xogando ao fútbol" Polaridade: Positivo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "O neno non está contento coas notas" Polaridade: example_title: Sentiment Analysis

• text: |- Extrae as entidades nomeadas do seguinte texto: Texto: "Chámome Wolfgang e vivo en Berlin" Entidades: Wolfgang:PER, Berlin:LOC

  Extrae as entidades nomeadas do seguinte texto: Texto: "María e Miguel non teñen ningún problema" Entidades: María:PER, Miguel:PER

  Extrae as entidades nomeadas do seguinte texto: Texto: "O mellor de Barcelona é o bar do meu amigo Pablo" Entidades: Pablo:PER, Barcelona:LOC

  Extrae as entidades nomeadas do seguinte texto: Texto: "Carlos comparte cuarto con Marc" Entidades: example_title: Name Entity Recognition (NER)
• text: A receita tradicional das filloas é example_title: Filloas
• text: O neno vivía preto de example_title: O neno base_model:
• meta-llama/Llama-3.1-8B pipeline_tag: text-generation library_name: transformers

---

Llama-3.1-Carballo

Table of Contents

<details> <summary>Click to expand</summary>

• Llama-3.1-Carballo
  • 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

</details>

Model description

Llama-3.1-Carballo is a 8B-parameter transformer-based causal language model for Galician, Portuguese, Spanish, Catalan and English. It is the result of a continual pretraining of meta-llama/Llama-3.1-8B with a multilingual corpus of almost 20B tokens, with an emphasis on Galician texts.

This model is part of the Carballo familily, a family of LLMs specialized in Galician. Smaller models can be found here

Intended uses and limitations

The Llama-3.1-Carballo 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"
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 5 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 (Catalan) or adapting existing language varieties (Portuguese-PT instead of Portuguese-BR).

The corpus is structured as follows:

Training hyperparameters

• seed: 42
• num_devices: 5
• train_batch_size: 4
• eval_batch_size: 4
• gradient_acummulation: 8
• 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 in the Galicia Supercomputing Center (CESGA), using 5 nodes with 2 GPUs NVIDIA A100 each one.

Evaluation

In process...

Additional information

Contact

For further information, please send an email to [email protected]

License

MIT License

Copyright (c) 2024 Proxecto Nós

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 research was funded by “The Nós project: Galician in the society and economy of Artificial Intelligence”, resulting from the agreement 2021-CP080 between the Xunta de Galicia and the University of Santiago de Compostela, and thanks to the Investigo program, within the National Recovery, Transformation and Resilience Plan, within the framework of the European Recovery Fund (NextGenerationEU).