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Model: MBZUAI/bactrian-x-llama-13b-merged Source: Original Platform
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README.md
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README.md
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---
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license: mit
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---
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#### Current Training Steps: 108,000
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This repo contains a merged model using low-rank adaptation (LoRA) for LLaMA-13b
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fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
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and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in 52 languages.
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### Dataset Creation
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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).
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2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
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3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
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<h3 align="center">
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<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
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</h3>
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### Training Parameters
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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).
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This version of the weights was trained with the following hyperparameters:
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- Epochs: 10
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- Batch size: 128
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- Cutoff length: 512
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- Learning rate: 3e-4
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- Lora _r_: 64
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- Lora target modules: q_proj, k_proj, v_proj, o_proj
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That is:
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```
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python finetune.py \
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--base_model='decapoda-research/llama-13b-hf' \
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--num_epochs=5 \
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--batch_size=128 \
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--cutoff_len=512 \
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--group_by_length \
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--output_dir='./bactrian-x-llama-13b-lora' \
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--lora_target_modules='q_proj,k_proj,v_proj,o_proj' \
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--lora_r=64 \
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--micro_batch_size=32
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```
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Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
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### Discussion of Biases
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(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
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### Citation Information
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```
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@misc{li2023bactrianx,
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title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation},
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author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin},
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year={2023},
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eprint={2305.15011},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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