TAME (TAiwan Mixture of Experts)

LLM for Taiwanese Culture across Diverse Domains

✍️ Online Demo
• 🤗 Model Collection • 🐦 Twitter/X • 📃 Model Paper • 📃 Eval Paper
• 👨️ Yen-Ting Lin

<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/vlfv5sHbt4hBxb3YwULlU.png" width="500">


<a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE">

Partnership with 和碩聯合科技, 長庚紀念醫院, 長春集團, 欣興電子, 律果, NVIDIA, 科技報橘

🌟 Demo Site

Try out Llama-3-Taiwan interactively at twllm.com

⚔️ Chatbot Arena

Participate in the exciting Chatbot Arena and compete against other chatbots!

🚀 Quick Start for Fine-tuning

Using Axolotl for fine-tuning:

# Run the axolotl docker image
docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest

# Preprocess datasets (optional but recommended)
CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess example_training_config_for_finetuning_twllm.yaml

# Fine-tune
accelerate launch -m axolotl.cli.train example_training_config_for_finetuning_twllm.yaml

Check out the example_training_config_for_finetuning_twllm.yaml file for detailed training configuration and parameters. For more training framework information, visit Axolotl's GitHub repository.

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🚀 We're excited to introduce Llama-3-Taiwan-70B! Llama-3-Taiwan-70B is a 70B parameter model finetuned on a large corpus of Traditional Mandarin and English data using the Llama-3 architecture. It demonstrates state-of-the-art performance on various Traditional Mandarin NLP benchmarks.

The model was trained with NVIDIA NeMo™ Framework using the NVIDIA Taipei-1 built with NVIDIA DGX H100 systems.

The compute and data for training Llama-3-Taiwan-70B was generously sponsored by Chang Gung Memorial Hospital, Chang Chun Group, Legalsign.ai, NVIDIA, Pegatron, TechOrange, and Unimicron (in alphabetical order).

We would like to acknowledge the contributions of our data provider, team members and advisors in the development of this model, including shasha77 for high-quality YouTube scripts and study materials, Taiwan AI Labs for providing local media content, Ubitus K.K. for offering gaming content, Professor Yun-Nung (Vivian) Chen for her guidance and advisement, Wei-Lin Chen for leading our pretraining data pipeline, Tzu-Han Lin for synthetic data generation, Chang-Sheng Kao for enhancing our synthetic data quality, and Kang-Chieh Chen for cleaning instruction-following data.

Model Summary

Llama-3-Taiwan-70B is a large language model finetuned for Traditional Mandarin and English users. It has strong capabilities in language understanding, generation, reasoning, and multi-turn dialogue. Key features include:

• 70B parameters
• Languages: Traditional Mandarin (zh-tw), English (en)
• Finetuned on High-quality Traditional Mandarin and English corpus covering general knowledge as well as industrial knowledge in legal, manufacturing, medical, and electronics domains
• 8K context length
• Open model released under the Llama-3 license

Training Details

• Training Framework: NVIDIA NeMo, NVIDIA NeMo Megatron
• Inference Framework: NVIDIA TensorRT-LLM
• Base model: Llama-3 70B
• Hardware: NVIDIA DGX H100 on Taipei-1
• Context length: 8K tokens (128k version)
• Batch size: 2M tokens per step

Evaluation

Checkout Open TW LLM Leaderboard for full and updated list.

Model	TMLU	Taiwan Truthful QA	Legal Eval	TW MT-Bench	Long context	Function Calling	TMMLU+
	學科知識	台灣在地化測試	台灣法律考題	中文多輪對答	長文本支援	函式呼叫
yentinglin/Llama-3-Taiwan-70B-Instruct	74.76%	80.95%	68.42%	7.54	128k version	✅	67.53%
yentinglin/Llama-3-Taiwan-70B-Instruct-DPO	74.60%	81.75%	70.33%	-	-	✅	-
yentinglin/Llama-3-Taiwan-70B-Instruct-128k	73.01%	80.16%	63.64%	-	-	✅	-
yentinglin/Llama-3-Taiwan-8B-Instruct	59.50%	61.11%	53.11%	7.21	128k version	✅	52.28%
yentinglin/Llama-3-Taiwan-8B-Instruct-DPO	59.88%	59.52%	52.63%	-	-	✅	-
yentinglin/Llama-3-Taiwan-8B-Instruct-128k	-	-	-	-	-	✅	-
Claude-3-Opus	73.59% (5-shot)	69.84%	60.29%	-	200k	✅	-
GPT4-o	65.56% (0-shot), 69.88% (5-shot)	76.98%	53.59%	-	128k	✅	-
GPT4-turbo	70.42% (5-shot)	-	-	-	128k	✅	60.34%^
Gemini-Pro	61.40% (5-shot)	-	-	-	1000k	✅	49.92%^
GPT-3.5-turbo-1106	49.37% (5-shot)	-	-	7.1	128k	✅	41.76%^
Qwen1.5-110B-Chat	75.69%	66.67%	49.28%	-	32k	✅	65.81%
Yi-34B-Chat	73.59%	71.43%	55.02%	6.9	200k	✅	64.10%
Meta-Llama-3-70B-Instruct	70.95%	65.08%	52.63%	-	8k	✅	62.75%
Mixtral-8x22B-Instruct-v0.1	55.57%	52.38%	44.98%	-	64k	✅	52.16%
Breexe-8x7B-Instruct-v0_1	-	-	-	7.2	8k	❓	48.92%
c4ai-command-r-plus	62.87%	64.29%	34.45%	-	128k	✅	49.75%
Meta-Llama-3-8B-Instruct	55.81%	46.83%	35.89%	-	8k	✅	43.38%
Breeze-7B-Instruct-v1_0	55.57%	52.38%	39.23%	6.0	32k	❓	41.77%
Llama3-TAIDE-LX-8B-Chat-Alpha1	47.30%	50.79%	37.80%	-	8k	❓	39.03%
Phi-3-mini-4k-instruct	40.97%	37.30%	27.27%	-	4k	❓	33.02%

Numbers are 0-shot by default.

Eval implementation

^ taken the closet matching numbers from original dataset.

Needle in a Haystack Evaluation

The "Needle in a 出師表" evaluation tests the model's ability to locate and recall important information embedded within a large body of text, using the classic Chinese text 《出師表》 by 諸葛亮.

To run the evaluation, use the script.

TW MT-Bench Score

• Average Score: 7.5375
• Maximum Score: 10
• Minimum Score: 1
• Median Score: 9.0
• Standard Deviation: 3.0349783771882133
• Total Number of Scores: 160
• Model response
• GPT-4 Eval
• Code fork from mtkresearch/TCEval with bug fixing

Use Cases

Llama-3-Taiwan-70B can be applied to a wide variety of NLP tasks in Traditional Mandarin and English, including:

1. 多輪對話

System: You are an AI assistant called Twllm, created by TAME (TAiwan Mixture of Expert) project.

User: 嗨，你好！

Assistant: 你好！我今天可以如何協助你？

User: 就想聊天而已....

Assistant: 當然，我很樂意和你聊天！有什麼特別想談論的嗎？

1. RAG（檢索增強生成）

Demo: 可以打開 Search Web on twllm.com

System: You are an AI assistant called Twllm, created by TAME (TAiwan Mixture of Expert) project.

User: 明天台大天氣如何？

Assistant: 根據天氣預報，明天台大天氣為陣雨。低溫 24°C。10 到 15 公里/小時 的 東 風 。降雨機率為 40%。

1. 格式化輸出, 語言理解, 實體識別, 函式呼叫

If you are interested in function-calling, I strongly recommend using constrained decoding to turn on json mode.

Example from HW7 in `INTRODUCTION