---
license: mit
---

#### Current Training Steps: 108,000


This repo contains a low-rank adapter (LoRA) for LLaMA-13b 
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in 52 languages.

### Dataset Creation

1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
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).

<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>

### Training Parameters

The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/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_proj, k_proj, v_proj, o_proj


That is:

```
python finetune.py \
    --base_model='decapoda-research/llama-13b-hf' \
    --num_epochs=5 \
    --batch_size=128 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./bactrian-x-llama-13b-lora' \
    --lora_target_modules='q_proj,k_proj,v_proj,o_proj' \
    --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{bactrian,
  author = {Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin},
  title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```