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README.md

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+---
+license: mit
+---
+
+This repo contains a low-rank adapter (LoRA) for BLOOM-7b1 
+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 Gujarati.
+
+### 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: 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-gu-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}
+}
+
+```