Huang Liang Hsun PRO

lianghsun

https://www.lianghsun.dev

AI & ML interests

Founder of 𝗧𝘄𝗶𝗻𝗸𝗹𝗲 𝗔𝗜. Focused on applying deep learning in legal and scientific domains, with expertise in NLP and model fine-tuning.

Recent Activity

liked a dataset about 9 hours ago

trendmicro-ailab/Primus-FineWeb

updated a model about 10 hours ago

lianghsun/Llama-3.3-70B-Taiwan-Cyber-Instruct

published a model about 10 hours ago

lianghsun/Llama-3.3-70B-Taiwan-Cyber-Instruct

View all activity

Organizations

lianghsun's activity

replied to their post 24 days ago

lol thanks! I’ve always wondered why HF posts don’t support markdown.

posted an update 24 days ago

Post

2281

With the arrival of Twinkle April — Twinkle AI’s annual open-source celebration held every April — our community is excited to unveil its very first project:

📊 Twinkle Eval (https://github.com/ai-twinkle/Eval), a next-generation evaluation tool led by our contributor @tedslin .

Unlike traditional evaluation tools like iKala’s ievals (https://github.com/ikala-ai/ievals), which can only evaluate language models (LMs) one sample at a time, Twinkle Eval is designed with Large Reasoning Models (LRMs) in mind. As reasoning time increases with more complex models, traditional tools become increasingly inefficient 😲 — for example, evaluating LRMs on the ikala/tmmluplus benchmark could take *
half a day without finishing.

One question we were especially curious about:
Does shuffling multiple-choice answer order impact model accuracy? 🤔
→ See: "Change Answer Order Can Decrease MMLU Accuracy" – arXiv:2406.19470v1

To address these challenges, Twinkle Eval brings three key innovations to the table:

1️⃣ Parallelized evaluation of samples
2️⃣ Multi-round testing for stability
3️⃣ Randomized answer order to test robustness

After running experiments, we observed that Twinkle Eval can speed up evaluation by up to 15× 🚀🚀. Interestingly, most models scored slightly lower under the 2️⃣3️⃣ test settings compared to their claimed performance — suggesting further benchmarking is needed.

This framework also comes with additional tunable parameters and detailed logging of LM behavior per question — perfect for those who want to dive deeper. 😆

If you find Twinkle Eval useful, please ⭐ the project and help spread the word 🤗

5 replies

posted an update 3 months ago

Post

2592

🖖 Let me introduce the work I've done over the past three months: 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕 and 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁, now open-sourced on 🤗 Hugging Face.

𝗹𝗶𝗮𝗻𝗴𝗵𝘀𝘂𝗻/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕: This model is built on top of 𝗺𝗲𝘁𝗮-𝗹𝗹𝗮𝗺𝗮/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝟯𝗕 with continual pretraining. The training dataset consists of a mixture of Traditional Chinese and multilingual texts in specific proportions, including 20B tokens of Traditional Chinese text.

𝗹𝗶𝗮𝗻𝗴𝗵𝘀𝘂𝗻/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁: This is a fine-tuned conversational model based on the foundation model.

This Llama-3.2-Taiwan open-source project is currently a one-person effort (yes, I did everything from text preparation — so exhausting!). If you're interested, feel free to join the Discord server for discussions.

🅱🅴🅽🅲🅷🅼🅰🆁🅺🅸🅽🅶

The evaluation was conducted using ikala/tmmluplus, though the README page does not yet reflect the latest results. The performance is close to the previous versions, indicating that further improvements might require adding more specialized knowledge in the datasets.

🅰 🅲🅰🅻🅻 🅵🅾🆁 🆂🆄🅿🅿🅾🆁🆃

If anyone is willing to provide compute resources, it would be greatly appreciated to help this project continue and grow. 💪

---
🏔️ Foundation model: lianghsun/Llama-3.2-Taiwan-3B
🤖 Instruction model: lianghsun/Llama-3.2-Taiwan-3B-Instruct
⚡ GGUF: lianghsun/Llama-3.2-Taiwan-3B-Instruct-GGUF

4 replies