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This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 fit on the Stanford-Alpaca-52k and databricks-dolly-15k data in Dutch.

Dataset Creation

  1. English Instructions: The English instuctions are obtained from alpaca-52k, and dolly-15k.
  2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
  3. 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: 8
  • Batch size: 128
  • Cutoff length: 1024
  • Learning rate: 3e-4
  • Lora r: 16
  • Lora target modules: query_key_value

That is:

python finetune.py \
    --base_model='bigscience/bloom-7b1' \
    --num_epochs=5 \
    --cutoff_len=1024 \
    --group_by_length \
    --output_dir='./bactrian-nl-bloom-7b1-lora' \
    --lora_target_modules='query_key_value' \
    --lora_r=16 \
    --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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