Transformers
Safetensors
Georgian
mbart
text2text-generation
trimmed
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metadata
language: kat
license: mit
tags:
  - trimmed
library_name: transformers
base_model: facebook/mbart-large-50
base_model_relation: quantized
datasets:
  - lbourdois/fineweb-2-trimming

mbart-large-50-kat-16384

This model is a 39.17% smaller version of facebook/mbart-large-50 optimized for Georgian language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.

Model Statistics

Metric Original Trimmed Reduction
Vocabulary size 250,027 tokens 16,384 tokens 93.45%
Model size 610,879,488 params 371,601,408 params 39.17%

image

Mining Dataset Statistics

Usage

from transformers import AutoModel, AutoTokenizer

model_name = "alphaedge-ai/mbart-large-50-kat-16384"
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Citations

mBART

@misc{tang2020multilingualtranslationextensiblemultilingual,
      title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, 
      author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan},
      year={2020},
      eprint={2008.00401},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2008.00401}, 
}

Trimming blog post

@misc{hf_blogpost_trimming,
      title={Introduction to Trimming}, 
      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
      year={2026},
      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming}, 
}