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MBZUAI / bactrian-x-mt5-xl-lora
Model's Last Updated: June 11 2023
Introduction of bactrian-x-mt5-xl-lora
Model Details of bactrian-x-mt5-xl-lora
Current Training Steps: 100,000
This repo contains a low-rank adapter (LoRA) for mt5-xl 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-turbofor 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-xl' \
--num_epochs=10 \
--batch_size=128 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-x-mt5-xl-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}
}
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