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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Traditional Mandarin LLMs for Taiwan
| Date | Stars |
|---|---|
| 2026-07-31 | 1419 |
| 2026-08-01 | 1419 |
| 2026-08-06 | 1419 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# TAME (TAiwan Mixture of Experts) <br/>LLM for Taiwanese Culture across Diverse Domains
<p align="center">
✍️ <a href="https://chat.twllm.com/" target="_blank">Online Demo</a>
•
🤗 <a href="https://huggingface.co/collections/yentinglin/taiwan-llm-6523f5a2d6ca498dc3810f07" target="_blank">Model Collection</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter/X</a> • 📃 <a href="https://arxiv.org/pdf/2311.17487.pdf" target="_blank">Model Paper</a> • 📃 <a href="https://arxiv.org/pdf/2403.20180" target="_blank">Eval Paper</a>
• 👨️ <a href="https://yentingl.com/" target="_blank">Yen-Ting Lin</a>
<br/><br/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/vlfv5sHbt4hBxb3YwULlU.png" width="500"> <br/>
<a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE">
<img src="https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg"></a>
<a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE">
<img src="https://img.shields.io/badge/Data_License-CC%20By%20NC%204.0-red.svg"></a>
<br/>
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
</p>
# 🌟 [Demo Site](https://twllm.com/)
Try out Llama-3-Taiwan interactively at [twllm.com](https://twllm.com/)
# ⚔️ [Chatbot Arena](https://arena.twllm.com/)
Participate in the exciting [Chatbot Arena](https://arena.twllm.com/) and compete against other chatbots!
# 🚀 Quick Start for Fine-tuning
Using [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for fine-tuning:
```bash
# 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](https://github.com/OpenAccess-AI-Collective/axolotl).
--------
🚀 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](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) using the NVIDIA Taipei-1 built with [NVIDIA DGX H100](https://www.nvidia.com/en-us/data-center/dgx-h100/) systems.
The compute and data for training Llama-3-Taiwan-70B was generously sponsored by [Chang Gung Memorial Hospital](https://www.cgmh.org.tw/eng), [Chang Chun Group](https://www.ccp.com.tw/ccpweb.nsf/homepage?openagent), [Legalsign.ai](https://legalsign.ai/), [NVIDIA](https://www.nvidia.com/zh-tw/), [Pegatron](https://www.pegatroncorp.com/), [TechOrange](https://buzzorange.com/techorange/), and [Unimicron](https://www.unimicron.com/) (in alphabetical order).
We would like to acknowledge the [contributions](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct#contributions) of our data provider, team members and advisors in the development of this model, including [shasha77](https://www.youtube.com/@shasha77) for high-quality YouTube scripts and study materials, [Taiwan AI Labs](https://ailabs.tw/) for providing local media content, [Ubitus K.K.](https://ubitus.net/zh/) 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 Excerpt of 19,492 characters
Read on GitHub
Yen-Ting Lin
57
9
4
China
2
Ikko Eltociear Ashimine · Japan
2
1
1
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:14ec98dbb8500741, topic:llm