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MBZUAI/
bactrian-x-mt5-large-lora

Name: bactrian-x-mt5-large-lora
Availability: InStock

2305.15011

mit

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Current Training Steps: 100,000

This repo contains a low-rank adapter (LoRA) for mt5-large fit on the Stanford-Alpaca-52k and databricks-dolly-15k data in 52 languages.

Dataset Creation

English Instructions: The English instuctions are obtained from alpaca-52k, and dolly-15k.
Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
Output Generation: We generate output from gpt-3.5-turbo for each language (conducted on April 2023).

Training Parameters

The code for training the model is provided in our github, which is adapted from Alpaca-LoRA. This version of the weights was trained with the following hyperparameters:

Epochs: 10
Batch size: 128
Cutoff length: 512
Learning rate: 3e-4
Lora r: 64
Lora target modules: q,v

That is:

python finetune.py \
    --base_model='google/mt5-large' \
    --num_epochs=10 \
    --batch_size=128 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./bactrian-x-mt5-large-lora' \
    --lora_target_modules='q,v' \
    --lora_r=64 \
    --micro_batch_size=32

Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.

Discussion of Biases

(1) Translation bias; (2) Potential English-culture bias in the translated dataset.

Citation Information

@misc{li2023bactrianx,
      title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, 
      author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin},
      year={2023},
      eprint={2305.15011},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

MBZUAI/bactrian-x-mt5-large-lora