h-j-han/Llama2-7B-VocADT-50k-Latin

VocADT is a solution for vocabulary adaptation using adapter modules that are trained to learn the optimal linear combination of existing embeddings while keeping the model’s weights fixed. VocADT offers a flexible and scalable solution without requiring external resources or language constraints.

New Vocabulary Adapted Models

Only the input/output embeddings are replaced, while all other original weights of base model remain fixed. These are the merged version: after training the adapters, we merge the original embeddings with the adapter to generate the new embeddings.

Name	Adapted Model	Base Model	New Vocab Size	Focused Languages
VocADT-Latin-Mistral	h-j-han/Mistral-7B-VocADT-50k-Latin	Mistral	50k	Swahili (sw), Indonesian (id), Estonian (et), Haitian Creole (ht), English (en)
VocADT-Mixed-Mistral	h-j-han/Mistral-7B-VocADT-50k-Mixed	Mistral	50k	Korean (ko), Greek (el), Russian (ru), Bulgarian (bg), English (en)
VocADT-Cyrillic-Mistral	h-j-han/Mistral-7B-VocADT-50k-Cyrillic	Mistral	50k	Russian (ru), Bulgarian (bg), Ukrainian (uk), Kazakh (kk), English (en)
VocADT-Latin-LLama	h-j-han/Llama2-7B-VocADT-50k-Latin	Llama	50k	Swahili (sw), Indonesian (id), Estonian (et), Haitian Creole (ht), English (en)
VocADT-Mixed-LLama	h-j-han/Llama2-7B-VocADT-50k-Mixed	Llama	50k	Korean (ko), Greek (el), Russian (ru), Bulgarian (bg), English (en)
VocADT-Cyrillic-LLama	h-j-han/Llama2-7B-VocADT-50k-Cyrillic	Llama	50k	Russian (ru), Bulgarian (bg), Ukrainian (uk), Kazakh (kk), English (en)

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

# model_name = "meta-llama/Llama-2-7b-hf" # Base Model
model_name = "h-j-han/Llama2-7B-VocADT-50k-Latin" # Vocabulary Adapted Model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prefix = "\nEnglish: Hello!\nSwahili: Habari!\nEnglish: What's your name?\nSwahili: Jina lako ni nani?\nEnglish: "
line = "My name is Amani."
suffix = f"\nSwahili:"
prompt = prefix + line + suffix

inputs = tokenizer(prompt, return_tensors="pt")
for item in inputs:
    inputs[item] = inputs[item].cuda()
outputs = model.generate(**inputs, max_new_tokens=5, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Reference

We provide code in Github repo : https://github.com/h-j-han/VocADT
Also, please find details in this paper :

@misc{han2024vocadt,
      title={Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?}, 
      author={HyoJung Han and Akiko Eriguchi and Haoran Xu and Hieu Hoang and Marine Carpuat and Huda Khayrallah},
      year={2024},
      eprint={2410.09644},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.09644}, 
}