Here’s a guide on Hugging Face‘s MaLA-500 language model.

MaLA-500 is a large language model that covers 534 languages and boasts an extended vocabulary size of 260,164, making it suitable for multilingual proficiency. It enhances its adaptation capabilities through continued pretraining and LoRA low-rank adaptation.

How to Get Started with the Model:
To get started with the MaLA-500 model, you will need the following requirements:
– transformers>=4.36.1
– peft>=0.6.2

Use the following Python code to get started with the model:
“`python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(‘meta-llama/Llama-2-7b-hf’)
base_model.resize_token_embeddings(260164)
tokenizer = AutoTokenizer.from_pretrained(‘MaLA-LM/mala-500’)
model = PeftModel.from_pretrained(base_model, ‘MaLA-LM/mala-500’)
“`

Citation:
If you use the MaLA-500 model in your work, please cite the following:
“`bibtex
@misc{lin2024mala500,
title={MaLA-500: Massive Language Adaptation of Large Language Models},
author={Peiqin Lin and Shaoxiong Ji and Jörg Tiedemann and André F. T. Martins and Hinrich Schütze},
year={2024},
eprint={2401.13303},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
“`

This guide provides you with the necessary information and code to get started with the MaLA-500 model from Hugging Face.

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Hugging Face MaLA-500 Model

MaLA-500 is a state-of-the-art language model designed to cover a vast range of 534 languages. This model is an extension of LLaMA 2 7B and incorporates continued pretraining with vocabulary expansion, featuring an increased vocabulary size of 260,164, and LoRA low-rank adaptation.

Key Features:
– Continued Pretraining: Enhances the model’s adaptability to a wide range of languages.
– LoRA Low-Rank Adaptation: Refines the model’s adaptation capabilities.
– Vocabulary Extension: MaLA-500 boasts an extended vocabulary size of 260,164.
– Multilingual Proficiency: Trained on Glot500-c, covering 534 languages.

How to Get Started with the Model
To get started with the MaLA-500 model, you will need the following requirements:

– transformers>=4.36.1
– peft>=0.6.2

Once you have the necessary requirements, use the following Python code to get started with the model:

“`python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(‘meta-llama/Llama-2-7b-hf’)
base_model.resize_token_embeddings(260164)
tokenizer = AutoTokenizer.from_pretrained(‘MaLA-LM/mala-500’)
model = PeftModel.from_pretrained(base_model, ‘MaLA-LM/mala-500’)
“`

Citation
If you use the MaLA-500 model in your work, please cite the following paper:

@misc{lin2024mala500,
title={MaLA-500: Massive Language Adaptation of Large Language Models},
author={Peiqin Lin and Shaoxiong Ji and Jörg Tiedemann and André F. T. Martins and Hinrich Schütze},
year={2024},
eprint={2401.13303},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

This citation ensures proper credit to the creators of the model.

MaLA-500 is a novel large language model designed to cover an extensive range of 534 languages. This model builds upon LLaMA 2 7B and integrates continued pretraining with vocabulary extension, with an expanded vocabulary size of 260,164, and LoRA low-rank adaptation.

  • Continued Pretraining: Enhances the model’s ability to adapt to a wide range of languages.
  • LoRA Low-Rank Adaptation: LoRA low-rank adaptation refines the model’s adaptation capabilities.
  • Vocabulary Extension: MaLA-500 boasts an extended vocabulary size of 260,164.
  • Multilingual Proficiency: Trained on Glot500-c, covering 534 languages.



How to Get Started with the Model

Requirements:

transformers>=4.36.1
peft>=0.6.2

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf')
base_model.resize_token_embeddings(260164)
tokenizer = AutoTokenizer.from_pretrained('MaLA-LM/mala-500')
model = PeftModel.from_pretrained(base_model, 'MaLA-LM/mala-500')



Citation

@misc{lin2024mala500,
      title={MaLA-500: Massive Language Adaptation of Large Language Models}, 
      author={Peiqin Lin and Shaoxiong Ji and Jörg Tiedemann and André F. T. Martins and Hinrich Schütze},
      year={2024},
      eprint={2401.13303},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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2024-01-31T18:48:17+01:00