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README.md
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---
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dataset_info:
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features:
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dtype: string
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dtype: string
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dtype: string
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- name: zh_id
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dtype: int64
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splits:
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- name: train
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num_bytes: 15132113806.438198
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num_examples: 5179546
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- name: validation
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num_bytes: 14820238.368907347
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num_examples: 5099
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download_size: 5569491261
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dataset_size: 15146934044.807106
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license: odc-by
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task_categories:
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- text-generation
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language:
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- zh
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pretty_name: Traditional Chinese C4
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size_categories:
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- 1M<n<10M
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duplicated_from: yentinglin/zh_TW_c4
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---
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# Language Models for Taiwanese Culture
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<p align="center">
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✍️ <a href="https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2" target="_blank">Online Demo</a>
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•
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🤗 <a href="https://huggingface.co/yentinglin" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/pdf/2305.13711.pdf" target="_blank">[Paper Coming Soon]</a>
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• 👨️ <a href="https://yentingl.com/" target="_blank">Yen-Ting Lin</a>
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<br/><br/>
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<img src="https://www.csie.ntu.edu.tw/~miulab/taiwan-llama/logo-v2.png" width="100"> <br/>
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<a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE">
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<img src="https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg"></a>
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<a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE">
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<img src="https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg"></a>
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<br/>
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</p>
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## Overview
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Taiwan-LLaMa is a full parameter fine-tuned model based on LLaMa 2 for Traditional Mandarin applications.
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**Taiwan-LLaMa v1.0** pretrained on over 5 billion tokens and instruction-tuned on over 490k conversations both in traditional mandarin.
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## Demo
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A live demonstration of the model can be accessed at [Hugging Face Spaces](https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2).
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## Key Features
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1. **Traditional Mandarin Support**: The model is fine-tuned to understand and generate text in Traditional Mandarin, making it suitable for Taiwanese culture and related applications.
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2. **Instruction-Tuned**: Further fine-tuned on conversational data to offer context-aware and instruction-following responses.
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3. **Performance on Vicuna Benchmark**: Taiwan-LLaMa's relative performance on Vicuna Benchmark is measured against models like GPT-4 and ChatGPT. It's particularly optimized for Taiwanese culture.
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4. **Flexible Customization**: Advanced options for controlling the model's behavior like system prompt, temperature, top-p, and top-k are available in the demo.
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## Work in progress
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- [ ] **Improved pretraining**: A refined pretraining process (e.g. more data from Taiwan, training strategies) is under development, aiming to enhance model performance for better Taiwanese culture.
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- [ ] **Extend max length**: Utilizing the Rope mechanism as described in [the paper](https://arxiv.org/abs/2104.09864), the model's length will be extended from 4k to 8k.
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## Taiwanese Culture Examples
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| Question | ChatGPT | **Taiwan LLaMa v1.0** | **Taiwan LLaMa v0.0 (w/o pretraining)** | Llama2_Traditional_Chinese_13b_Chat |
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| 以惡地景觀著名的是高雄哪一個景點? | 以惡地地景著名的是高雄市的旗津。 | 高雄以擁有惡地景觀著名的景點是田寮月世界。✅ | 以惡地景觀著名的是高雄市的旗津島。旗津島是一個小島,... | 高雄市內有多個以惡地景觀著名的景點,其中以下面幾個最為著名: 1. 高雄捷運紅線沿途景點:高雄市政府為了推廣高雄 |
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| 唐鳳是誰? | 唐鳳(Audrey Tang)是台灣的政治家、社會活動家和軟體工程師。她於2016年擔任台灣行政院數位政務委員,是台灣政府歷史上首位沒有政黨背景的政務官。唐鳳在軟體工程領域有豐富的經驗。 ✅ | 唐鳳是政治評論家、資訊社會學研究者,也是台灣第一位擔任政府數位政委的人。 ✅ | 唐鳳(Tang Feng)是一位著名的中國演員和歌手。他出生於1974年12月17日,出生地點在中國北京。唐鳳在1995年開始他的演藝生涯,並在同年發行了他的首張專輯《我不孤單》。此後,他發行了許多受歡迎的歌曲,並在多部電視劇和電影中擔任主角。唐鳳因其精湛的演技和迷人的歌聲而被譽為“亞洲魅力男王”。他的音樂獲得了多個獎項,包括“華表最佳新男演唱獎”、“華表最佳歌曲獎”和“華表最佳舞蹈獎”等。 | 唐鳳是唐朝時期的一位著名文學家,他的代表作品包括《陰騭》、《狂凡子》等。他的文學風格具有強烈的個性和創意,被譽為「唐代文學之父」。 |
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| NTU 在哪? | NTU位於新加坡,具體地址是50 Nanyang Avenue, Singapore 639798。 | NTU 是國立臺灣大學的縮寫。它是臺灣的一所頂尖研究型大學,位於臺北市。 ✅| NTU 是台灣的一���大學,位於台北市。✅ | NTU 的總部位於台灣新北市板橋區,佔地面積約為1,000公頃。|
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| 甚麼是22K? | "22K" 可能指的是金的純度,尤其是在珠寶和金飾方面。 | "22K"是指台灣的大學畢業生的起薪,大約是新台幣22,000元。這個詞被用來形容大學畢業生的起薪偏低,並且在就業市場上對於大學畢業生的需求不足。✅| 22K是指溫度尺度上的22,000K(千度)。1K代表一度,即溫度差距,是攝氏溫標(攝氏度)的一百萬分之一。因此,22K等於-253.15°C(攝氏溫標)或-423.67°F(華氏溫標)。 | 22K是一種簡寫,即22,000。它是一個數字,表示22,000個單位。|
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| 肉圓要不要加香菜? | 這是個個人口味的問題,有些人喜歡在肉圓中加入香菜,因為香菜的清香可以增添風味;而有些人則不喜歡香菜的味道,覺得它會蓋過其他食材的味道。所以要不要加香菜,可以根據個人喜好來決定。 | 這是一個個人喜好的問題,有些人喜歡在肉圓裡加香菜,有些人不喜歡。**在台灣**,肉圓加香菜是一種常見的做法,這是一種在肉圓上加香菜的做法。然而,在其他國這是一個文化差異的問題。😊 | 肉圓加香菜可以增添一些芳香和美味的口感,如果你喜歡香菜的味道,可以試試加入肉圓中。香菜通常在製作過程中炒香,可以增添一些芳香和美味的味道。如果你想要加入香菜,可以在製作過程中炒香一些香菜,然後將其混入肉圓中。肉圓加香菜可以增添一些芳香和美味的口感,如果你喜歡香菜的味道,可以試試加入肉圓中。| 加香菜的肉圓味道更好,但是不加香菜的肉圓也很好吃。如果你喜歡香菜的味道,那麼加上香菜就好了。如果你不喜歡香菜,那麼不加也不會影響肉 |
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## Model
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We provide a number of model checkpoints that we trained. Please find them on Hugging Face [here](https://huggingface.co/models?search=taiwan-llama). Here are some quick links to the checkpoints that are finetuned from LLaMa 2:
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| **Model** | **13B** |
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| **Taiwan-LLaMa v1.0** (_better for Taiwanese Culture_) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v1.0" target="_blank">yentinglin/Taiwan-LLaMa-v1.0</a> |
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| Taiwan-LLaMa v0.9 (partial instruction set) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.9" target="_blank">yentinglin/Taiwan-LLaMa-v0.9</a> |
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| Taiwan-LLaMa v0.0 (no Traditional Mandarin pretraining) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.0" target="_blank">yentinglin/Taiwan-LLaMa-v0.0</a> |
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## Data
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Here are some quick links to the datasets that we used to train the models:
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| **Dataset** | **Link** |
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| **Instruction-tuning** | 🤗 <a href="https://huggingface.co/datasets/yentinglin/traditional_mandarin_instructions" target="_blank">yentinglin/traditional_mandarin_instructions</a> |
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| Traditional Mandarin Pretraining | 🤗 <a href="https://huggingface.co/datasets/yentinglin/zh_TW_c4" target="_blank">yentinglin/zh_TW_c4</a> |
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## Architecture
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Taiwan-LLaMa is based on LLaMa 2, leveraging transformer architecture, <a href="https://github.com/Dao-AILab/flash-attention" target="_blank">flash attention 2</a>, and bfloat16.
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It includes:
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* Pretraining Phase: Pretrained on a vast corpus of over 5 billion tokens, extracted from common crawl in Traditional Mandarin.
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* Fine-tuning Phase: Further instruction-tuned on over 490k multi-turn conversational data to enable more instruction-following and context-aware responses.
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## Generic Capabilities on Vicuna Benchmark
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The data is translated into traditional mandarin for evaluating the general capability.
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<img src="./images/zhtw_vicuna_bench_chatgptbaseline.png" width="700">
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The scores are calculated with ChatGPT as the baseline, represented as 100%. The other values show the relative performance of different models compared to ChatGPT.
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| Language Model | Relative Score (%) |
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| GPT-4 | 102.59% |
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| ChatGPT | 100.00% |
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| **Taiwan-LLaMa v1.0** | 76.76% |
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| Claude-Instant-1.2 | 74.04% |
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| Llama2_Traditional_Chinese_13b_Chat | 56.21% |
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## How to deploy the model on my own machine?
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We recommend hosting models with [🤗 Text Generation Inference](https://github.com/huggingface/text-generation-inference). Please see their [license](https://github.com/huggingface/text-generation-inference/blob/main/LICENSE) for details on usage and limitations.
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```bash
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bash run_text_generation_inference.sh "yentinglin/Taiwan-LLaMa" NUM_GPUS DIR_TO_SAVE_MODEL PORT MAX_INPUT_LEN MODEL_MAX_LEN
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```
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Prompt format follows vicuna-v1.1 template:
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```
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A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user} ASSISTANT:
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```
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## Setup development environment
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```bash
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conda create -n taiwan-llama python=3.10 -y
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conda activate taiwan-llama
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pip install -r requirements.txt
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```
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## Citations
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If you use our code, data, or models in your research, please cite this repository. You can use the following BibTeX entry:
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```bibtex
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@inproceedings{lin-chen-2023-llm,
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title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models",
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author = "Lin, Yen-Ting and Chen, Yun-Nung",
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booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)",
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month = jul,
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year = "2023",
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address = "Toronto, Canada",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2023.nlp4convai-1.5",
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pages = "47--58"
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}
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@misc{taiwanllama,
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author={Lin, Yen-Ting and Chen, Yun-Nung},
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title={Taiwanese-Aligned Language Models based on Meta-Llama2},
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year={2023},
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url={https://github.com/adamlin120/Taiwan-LLaMa},
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note={Code and models available at https://github.com/adamlin120/Taiwan-LLaMa},
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}
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```
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## Collaborate With Us
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If you are interested in contributing to the development of Traditional Mandarin language models, exploring new applications, or leveraging Taiwan-LLaMa for your specific needs, please don't hesitate to contact us. We welcome collaborations from academia, industry, and individual contributors.
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## License
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The code in this project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.
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The models included in this project are licensed under the LLAMA 2 Community License. See the [LLAMA2 License](https://github.com/facebookresearch/llama/blob/main/LICENSE) for full details.
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## OpenAI Data Acknowledgment
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The data included in this project were generated using OpenAI's models and are subject to OpenAI's Terms of Use. Please review [OpenAI's Terms of Use](https://openai.com/policies/terms-of-use) for details on usage and limitations.
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## Acknowledgements
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We thank [Meta LLaMA team](https://github.com/facebookresearch/llama) and [Vicuna team](https://github.com/lm-sys/FastChat) for their open-source efforts in democratizing large language models.
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data/train-00000-of-00031-3f39a102ab83d07c.parquet → default/yentinglin-zh_tw_c4-train-00000-of-00031.parquet
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data/train-00001-of-00031-11e67f9621ccfa66.parquet → default/yentinglin-zh_tw_c4-train-00001-of-00031.parquet
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data/train-00002-of-00031-1f3b5f20c0c525af.parquet → default/yentinglin-zh_tw_c4-train-00002-of-00031.parquet
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data/train-00003-of-00031-ab11b74823d4c40b.parquet → default/yentinglin-zh_tw_c4-train-00003-of-00031.parquet
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data/train-00004-of-00031-05a6f6da6e2a1cc6.parquet → default/yentinglin-zh_tw_c4-train-00004-of-00031.parquet
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data/train-00005-of-00031-cee6464d907be215.parquet → default/yentinglin-zh_tw_c4-train-00005-of-00031.parquet
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data/train-00007-of-00031-ea485aa4a1c5dcfd.parquet → default/yentinglin-zh_tw_c4-train-00007-of-00031.parquet
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data/train-00022-of-00031-0dcf59276dec6da4.parquet → default/yentinglin-zh_tw_c4-train-00022-of-00031.parquet
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