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README.md

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+---
+license: mit
+---
+
+#### Current Training Steps: 100,000
+
+
+This repo contains a low-rank adapter (LoRA) for mt5-xl
+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,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}
+}
+```