|
--- |
|
license: llama3 |
|
library_name: transformers |
|
pipeline_tag: text-generation |
|
base_model: meta-llama/Meta-Llama-3-70B |
|
language: |
|
- zh |
|
- en |
|
tags: |
|
- zhtw |
|
widget: |
|
- text: >- |
|
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: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT: |
|
|
|
--- |
|
|
|
<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/vlfv5sHbt4hBxb3YwULlU.png" alt="Taiwan LLM Logo" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
|
|
|
# 🚀 [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! |
|
|
|
🚀 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-rc1#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 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](https://www.nvidia.com/zh-tw/ai-data-science/products/nemo/), [NVIDIA NeMo Megatron](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/megatron.html) |
|
- Inference Framework: [NVIDIA TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) |
|
- Base model: [Llama-3 70B](https://llama.meta.com/llama3/) |
|
- Hardware: [NVIDIA DGX H100](https://www.nvidia.com/zh-tw/data-center/dgx-h100/) on Taipei-1 |
|
- Context length: 8K tokens ([128k version](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-128k)) |
|
- Batch size: 2M tokens per step |
|
|
|
# Evaluation |
|
|
|
Checkout [Open TW LLM Leaderboard](https://huggingface.co/spaces/yentinglin/open-tw-llm-leaderboard) for full and updated list. |
|
|
|
| Model | [TMLU](https://arxiv.org/pdf/2403.20180) | Taiwan Truthful QA | [Legal Eval](https://huggingface.co/datasets/lianghsun/tw-legal-benchmark-v1) | [TW MT-Bench](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2) | Long context | Function Calling | [TMMLU+](https://github.com/iKala/ievals) | |
|
|---------------------------------------------------------------------------------|--------------|---------------|--------------------|--------------|--------------|-----------------|-----------| |
|
| | 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 | 函數呼叫 | | |
|
| [**yentinglin/Llama-3-Taiwan-70B-Instruct**](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct) | **74.76%** | 80.95% | 68.42% | 7.54 | [128k version](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-128k) | ✅ | 67.53% | |
|
| [**yentinglin/Llama-3-Taiwan-70B-Instruct-DPO**](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-DPO) | 74.60% | **81.75%** | **70.33%** | - | - | ✅ | - | |
|
| [**yentinglin/Llama-3-Taiwan-70B-Instruct-128k**](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct) | 73.01% | 80.16% | 63.64% | - | - | ✅ | - | |
|
| [**yentinglin/Llama-3-Taiwan-8B-Instruct**](https://huggingface.co/yentinglin/Llama-3-Taiwan-8B-Instruct) | 59.50% | 61.11% | 53.11% | 7.21 | [128k version](https://huggingface.co/yentinglin/Llama-3-Taiwan-8B-Instruct-128k) | ✅ | 52.28% | |
|
| [**yentinglin/Llama-3-Taiwan-8B-Instruct-DPO**](https://huggingface.co/yentinglin/Llama-3-Taiwan-8B-Instruct-DPO) | 59.88% | 59.52% | 52.63% | - | - | ✅ | - | |
|
| [**yentinglin/Llama-3-Taiwan-8B-Instruct-128k**](https://huggingface.co/yentinglin/Llama-3-Taiwan-8B-Instruct-128k) | - | - | - | - | - | ✅ | - | |
|
| [Claude-3-Opus](https://www.anthropic.com/api) | [73.59% (5-shot)](https://arxiv.org/pdf/2403.20180) | [69.84%](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-rc3/tree/main/opus-Taiwan-Truthful-QA) | [60.29%](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-rc3/tree/main/opus) | - | 200k | ✅ | - | |
|
| [GPT4-o](https://platform.openai.com/docs/api-reference/chat/create) | [65.56% (0-shot), 69.88% (5-shot)](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-rc3/tree/main/4o-tmlu) | [76.98%](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-rc3/tree/main/4o-Taiwan-Truthful-QA) | [53.59%](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-rc3/tree/main/4o) | - | 128k | ✅ | - | |
|
| [GPT4-turbo](https://platform.openai.com/docs/api-reference/chat/create) | [70.42% (5-shot)](https://arxiv.org/pdf/2403.20180) | - | - | - | 128k | ✅ | 60.34%^ | |
|
| [Gemini-Pro](https://ai.google.dev/gemini-api/docs) | [61.40% (5-shot)](https://arxiv.org/pdf/2403.20180) | - | - | - | 1000k | ✅ | 49.92%^ | |
|
| [GPT-3.5-turbo-1106](https://platform.openai.com/docs/api-reference/chat/create) | [49.37% (5-shot)](https://arxiv.org/pdf/2403.20180) | - | - | 7.1 | 128k | ✅ | 41.76%^ | |
|
| [Qwen1.5-110B-Chat](https://huggingface.co/Qwen/Qwen1.5-110B-Chat) | **75.69%** | 66.67% | 49.28% | - | 32k | ✅ | 65.81% | |
|
| [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 73.59% | 71.43% | 55.02% | 6.9 | 200k | ✅ | 64.10% | |
|
| [Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | 70.95% | 65.08% | 52.63% | - | 8k | ✅ | 62.75% | |
|
| [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) | 55.57% | 52.38% | 44.98% | - | 64k | ✅ | 52.16% | |
|
| [Breexe-8x7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breexe-8x7B-Instruct-v0_1) | - | - | - | 7.2 | 8k | ❓ | 48.92% | |
|
| [c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus) | 62.87% | 64.29% | 34.45% | - | 128k | ✅ | 49.75% | |
|
| [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | 55.81% | 46.83% | 35.89% | - | 8k | ✅ | 43.38% | |
|
| [Breeze-7B-Instruct-v1_0](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0) | 55.57% | 52.38% | 39.23% | 6.0 | 32k | ❓ | 41.77% | |
|
| [Llama3-TAIDE-LX-8B-Chat-Alpha1](https://huggingface.co/taide/Llama3-TAIDE-LX-8B-Chat-Alpha1) | 47.30% | 50.79% | 37.80% | - | 8k | ❓ | 39.03% | |
|
| [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) | 40.97% | 37.30% | 27.27% | - | 4k | ❓ | 33.02% | |
|
|
|
Numbers are 0-shot by default. |
|
|
|
[Eval implementation](https://github.com/adamlin120/lm-evaluation-harness) |
|
|
|
^ 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](https://github.com/adamlin120/needle-haystack/tree/main). |
|
|
|
|
|
# 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 resopnse](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-rc1/blob/main/Llama-3-Taiwan.jsonl) |
|
- [GPT-4 Eval](https://huggingface.co/yentinglin/Llama-3-Taiwan-70B-Instruct-rc1/blob/main/gpt-4_single.jsonl) |
|
- [Code fork from `mtkresearch/TCEval` with bug fixing](https://github.com/adamlin120/TCEval) |
|
|
|
|
|
 |
|
|
|
|
|
# Use Cases |
|
|
|
Llama-3-Taiwan-70B can be applied to a wide variety of NLP tasks in Traditional Chinese and English, including: |
|
|
|
<details> |
|
<summary>1. 多輪對話</summary> |
|
|
|
<pre> |
|
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？ |
|
</pre> |
|
|
|
</details> |
|
|
|
<details> |
|
<summary>2. RAG（檢索增強生成）</summary> |
|
|
|
Demo: 可以打開 `Search Web` on [twllm.com](https://twllm.com/) |
|
|
|
|
|
 |
|
|
|
|
|
 |
|
|
|
<pre> |
|
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%。 |
|
</pre> |
|
|
|
</details> |
|
|
|
<details> |
|
<summary>3. 格式化輸出, 語言理解, 實體識別, 函數調用</summary> |
|
|
|
If you are interested in function-calling, I strongly recommend [using constrained decoding](https://huggingface.co/docs/text-generation-inference/basic_tutorials/using_guidance) to turn on `json mode`. |
|
|
|
Example from HW7 in `INTRODUCTION TO GENERATIVE AI 2024 SPRING` from `HUNG-YI LEE (李宏毅)` |
|
|
|
|
|
 |
|
|
|
<pre> |
|
System: You are an AI assistant called Twllm, created by TAME (TAiwan Mixture of Expert) project. |
|
|
|
User: 您是電影評論分析師，負責情緒分析。 |
|
分析每個被評論的句子，評估句子中每個字和標點符號的重要性， |
|
並且將結果使用 tuple 的結構列出。 |
|
tuple 值的範圍介於 -1 (負向) 到 1 (正向) 之間， |
|
然後，將評論分類為正向 (1) 或負向 (-1)。 |
|
這些需要被評論的句子，包含在 <review> 標籤中間。 |
|
|
|
輸出範例: |
|
[(<單字或標點符號>, <float importance>), ...] |
|
<int classification> |
|
<review> 根據外媒報導，宏都拉斯在去年3月與台灣斷交，並轉往與中國建交之後，卻遲遲等不到中國的金援，談好要進口宏都拉斯的白蝦也拖延許久，甚至還將白蝦價格殺價至過往與台灣交易的半價，再加上墨西哥同時也中斷了與宏國的白蝦貿易，對當地蝦農可說是雪上加霜。 <review> |
|
|
|
|
|
Assistant: [('根據', 0.2), ('外媒', 0.1), ('報導', -0.1), (',', 0.05), ('宏都拉斯', -0.3), ('在去年3月與台灣斷交， ', -0.5), ('並轉往與中國建交之後， ', -0.4), ('卻遲遲等不到中國的金援， ', -1), |
|
('談好要進口宏都拉斯的白蝦也拖延許久， ', -1), |
|
(',甚至還將白蝦價格殺價至過往與台灣交易的半價，再加上墨西哥同時也中斷了與宏國的白蝦貿易，對當地蝦農可說是雪上加霜。 ',-1)] |
|
-1 |
|
|
|
</pre> |
|
</details> |
|
|
|
|
|
|
|
|
|
|
|
# Get Started |
|
|
|
*Caveat: System message should always be set.* |
|
|
|
## Hugging Face Transformers library |
|
You can use Llama-3-Taiwan-70B with the Hugging Face Transformers library: |
|
|
|
|
|
```python |
|
import torch |
|
from transformers import pipeline, StoppingCriteria |
|
|
|
# Define a custom stopping criteria class |
|
class EosListStoppingCriteria(StoppingCriteria): |
|
def __init__(self, eos_sequence=[128256]): |
|
self.eos_sequence = eos_sequence |
|
|
|
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: |
|
last_ids = input_ids[:, -len(self.eos_sequence):].tolist() |
|
return self.eos_sequence in last_ids |
|
|
|
# Initialize the model with automatic device mapping |
|
llm = pipeline("text-generation", model="yentinglin/Llama-3-Taiwan-70B-Instruct-rc1", device_map="auto") |
|
tokenizer = llm.tokenizer |
|
|
|
# Define a conversation example |
|
chat = [ |
|
{"role": "system", "content": "You are an AI assistant called Twllm, created by TAME (TAiwan Mixture of Expert) project."}, |
|
{"role": "user", "content": "你好，請問你可以完成什麼任務？"}, |
|
{"role": "assistant", "content": "你好，我可以幫助您解決各種問題、提供資訊並協助完成多種任務。例如：回答技術問題、提供建議、翻譯文字、尋找資料或協助您安排行程等。請告訴我如何能幫助您。"}, |
|
{"role": "user", "content": "太棒了！"} |
|
] |
|
flatten_chat_for_generation = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
|
""" |
|
<|im_start|>user |
|
You are an AI assistant called Twllm, created by TAME (TAiwan Mixture of Expert) project.<|im_end|> |
|
<|im_start|>user |
|
你好，請問你可以完成什麼任務？<|im_end|> |
|
<|im_start|>assistant |
|
你好，我可以幫助您解決各種問題、提供資訊和協助您完成許多不同的任務。例如：回答技術問題、提供建議、翻譯文字、尋找資料或協助您安排行程等。請告訴我如何能幫助您。<|im_end|> |
|
<|im_start|>user |
|
太棒了！<|im_end|> |
|
<|im_start|>assistant |
|
|
|
""" |
|
|
|
# Generate a response using the custom stopping criteria |
|
output = llm(flatten_chat_for_generation, return_full_text=False, max_new_tokens=128, top_p=0.9, temperature=0.7, stopping_criteria=[EosListStoppingCriteria([tokenizer.eos_token_id])]) |
|
print(output[0]['generated_text']) |
|
"謝謝！很高興能夠為您服務。如果有任何其他需要協助的地方，請隨時與我聯繫。我會盡最大努力為您提供所需的支援。" |
|
``` |
|
|
|
## vLLM |
|
|
|
Start the server |
|
```bash |
|
export NUM_GPUS=4 |
|
export PORT=8000 |
|
|
|
docker run \ |
|
-e HF_TOKEN=$HF_TOKEN \ |
|
--gpus '"device=0,1,2,3"' \ |
|
-v ~/.cache/huggingface:/root/.cache/huggingface \ |
|
-p "${PORT}:8000" \ |
|
--ipc=host \ |
|
vllm/vllm-openai:v0.4.0.post1 \ |
|
--model "yentinglin/Llama-3-Taiwan-70B-Instruct-rc1" \ |
|
-tp "${NUM_GPUS}" |
|
``` |
|
|
|
Sample client code, or you can use anything OpenAI-API compatible clients |
|
|
|
```python |
|
# pip install "openai>=1.0.0" |
|
from openai import OpenAI |
|
# Set OpenAI's API key and API base to use vLLM's API server. |
|
openai_api_key = "EMPTY" |
|
openai_api_base = "http://localhost:8000/v1" |
|
|
|
client = OpenAI( |
|
api_key=openai_api_key, |
|
base_url=openai_api_base, |
|
) |
|
|
|
chat_response = client.chat.completions.create( |
|
model="yentinglin/Llama-3-Taiwan-70B-Instruct-rc1", |
|
messages=[ |
|
{"role": "system", "content": "You are a helpful assistant."}, |
|
{"role": "user", "content": "Tell me a joke."}, |
|
] |
|
) |
|
print("Chat response:", chat_response) |
|
``` |
|
|
|
|
|
Enjoy exploring the capabilities of Llama-3-Taiwan-70B! We look forward to seeing what you create with this powerful open-source model. If you have any questions or feedback, please let us know. |
|
|
|
# Contributions |
|
- [**Professor Yun-Nung (Vivian) Chen**](https://www.csie.ntu.edu.tw/~yvchen/), for her guidance and advisement throughout the project. |
|
- [**Wei-Lin Chen**](mailto:[email protected]), for leading our pretraining data pipeline. |
|
- [**Tzu-Han Lin**](mailto:[email protected]), for synthetic data generation. |
|
- [**Chang-Sheng Kao**](mailto:[email protected]), for enhancing our synthetic data quality. |
|
- [**Kang-Chieh Chen**](mailto:[email protected]), for cleaning instruction-following data. |
|
- [**Min-Yi Chen**](mailto:[email protected]) and [**Shao-Heng Hsu**](mailto:[email protected]), for collecting chemical engineering data and benchmarks. |
|
- Chung-Yao Ma, Jonathan Guo and Kai-Chun Chang, for collecting manufacturing and electrical engineering data and benchmarks, and project progress management |
|
|
|
# Citation |
|
``` |
|
@article{DBLP:journals/corr/abs-2311-17487, |
|
author = {Yen{-}Ting Lin and |
|
Yun{-}Nung Chen}, |
|
title = {Taiwan {LLM:} Bridging the Linguistic Divide with a Culturally Aligned |
|
Language Model}, |
|
journal = {CoRR}, |
|
volume = {abs/2311.17487}, |
|
year = {2023}, |
|
url = {https://doi.org/10.48550/arXiv.2311.17487}, |
|
doi = {10.48550/ARXIV.2311.17487}, |
|
eprinttype = {arXiv}, |
|
eprint = {2311.17487}, |
|
timestamp = {Tue, 05 Dec 2023 14:40:42 +0100}, |
|
biburl = {https://dblp.org/rec/journals/corr/abs-2311-17487.bib}, |
|
bibsource = {dblp computer science bibliography, https://dblp.org} |
|
} |
|
@article{DBLP:journals/corr/abs-2403-20180, |
|
author = {Po{-}Heng Chen and |
|
Sijia Cheng and |
|
Wei{-}Lin Chen and |
|
Yen{-}Ting Lin and |
|
Yun{-}Nung Chen}, |
|
title = {Measuring Taiwanese Mandarin Language Understanding}, |
|
journal = {CoRR}, |
|
volume = {abs/2403.20180}, |
|
year = {2024}, |
|
url = {https://doi.org/10.48550/arXiv.2403.20180}, |
|
doi = {10.48550/ARXIV.2403.20180}, |
|
eprinttype = {arXiv}, |
|
eprint = {2403.20180}, |
|
timestamp = {Wed, 10 Apr 2024 17:37:45 +0200}, |
|
biburl = {https://dblp.org/rec/journals/corr/abs-2403-20180.bib}, |
|
bibsource = {dblp computer science bibliography, https://dblp.org} |
|
} |
|
``` |
