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---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: apache-2.0
language:
- zh
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:
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Acknowledge license to accept the repository.
extra_gated_prompt: Please contact the author for access.
extra_gated_button_content: Acknowledge license 同意以上內容
extra_gated_fields:
Name: text
Mail: text
Organization: text
Country: text
Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox
使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者: checkbox
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
# Model Card for Taiwan LLM 13B v2.0 chat
Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.
Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning.
This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances.
It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance.
For detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf).
## Model description
- **Model type:** A 13B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw)
- **Finetuned from model:** [yentinglin/Taiwan-LLM-13B-v2.0-base](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-base)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa
- **Demo:** https://twllm.com/
## Performance
TMMLUS+ score: 24.76727075757576
## Intended uses
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# pip install transformers>=4.34
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="yentinglin/Taiwan-LLM-13B-v2.0-chat", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "你是一個人工智慧助理",
},
{"role": "user", "content": "東北季風如何影響台灣氣候？"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5.0
## Citation
If you find Taiwan LLM is useful in your work, please cite it with:
```
@misc{lin2023taiwan,
title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model},
author={Yen-Ting Lin and Yun-Nung Chen},
year={2023},
eprint={2311.17487},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# Acknowledgement
Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project.
## Open LLM Leaderboard
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------------|------:|--------------|-----:|---|-----:|
|leaderboard:arc:challenge:25 | 0|acc |0.5529|± |0.0145|
| | |acc_norm |0.5862|± |0.0144|
|leaderboard:gsm8k:5 | 0|qem |0.3177|± |0.0128|
|leaderboard:hellaswag:10 | 0|acc |0.6307|± |0.0048|
| | |acc_norm |0.8327|± |0.0037|
|leaderboard:mmlu:_average:5 | |acc |0.5483|± |0.0356|
|leaderboard:mmlu:abstract_algebra:5 | 0|acc |0.3400|± |0.0476|
|leaderboard:mmlu:anatomy:5 | 0|acc |0.5111|± |0.0432|
|leaderboard:mmlu:astronomy:5 | 0|acc |0.5789|± |0.0402|
|leaderboard:mmlu:business_ethics:5 | 0|acc |0.5100|± |0.0502|
|leaderboard:mmlu:clinical_knowledge:5 | 0|acc |0.6000|± |0.0302|
|leaderboard:mmlu:college_biology:5 | 0|acc |0.5764|± |0.0413|
|leaderboard:mmlu:college_chemistry:5 | 0|acc |0.4100|± |0.0494|
|leaderboard:mmlu:college_computer_science:5 | 0|acc |0.4500|± |0.0500|
|leaderboard:mmlu:college_mathematics:5 | 0|acc |0.3800|± |0.0488|
|leaderboard:mmlu:college_medicine:5 | 0|acc |0.5434|± |0.0380|
|leaderboard:mmlu:college_physics:5 | 0|acc |0.2941|± |0.0453|
|leaderboard:mmlu:computer_security:5 | 0|acc |0.7000|± |0.0461|
|leaderboard:mmlu:conceptual_physics:5 | 0|acc |0.4468|± |0.0325|
|leaderboard:mmlu:econometrics:5 | 0|acc |0.2719|± |0.0419|
|leaderboard:mmlu:electrical_engineering:5 | 0|acc |0.4552|± |0.0415|
|leaderboard:mmlu:elementary_mathematics:5 | 0|acc |0.3175|± |0.0240|
|leaderboard:mmlu:formal_logic:5 | 0|acc |0.3413|± |0.0424|
|leaderboard:mmlu:global_facts:5 | 0|acc |0.3700|± |0.0485|
|leaderboard:mmlu:high_school_biology:5 | 0|acc |0.6323|± |0.0274|
|leaderboard:mmlu:high_school_chemistry:5 | 0|acc |0.4581|± |0.0351|
|leaderboard:mmlu:high_school_computer_science:5 | 0|acc |0.5400|± |0.0501|
|leaderboard:mmlu:high_school_european_history:5 | 0|acc |0.6364|± |0.0376|
|leaderboard:mmlu:high_school_geography:5 | 0|acc |0.6970|± |0.0327|
|leaderboard:mmlu:high_school_government_and_politics:5| 0|acc |0.7617|± |0.0307|
|leaderboard:mmlu:high_school_macroeconomics:5 | 0|acc |0.4974|± |0.0254|
|leaderboard:mmlu:high_school_mathematics:5 | 0|acc |0.3296|± |0.0287|
|leaderboard:mmlu:high_school_microeconomics:5 | 0|acc |0.5336|± |0.0324|
|leaderboard:mmlu:high_school_physics:5 | 0|acc |0.3709|± |0.0394|
|leaderboard:mmlu:high_school_psychology:5 | 0|acc |0.7468|± |0.0186|
|leaderboard:mmlu:high_school_statistics:5 | 0|acc |0.4074|± |0.0335|
|leaderboard:mmlu:high_school_us_history:5 | 0|acc |0.7108|± |0.0318|
|leaderboard:mmlu:high_school_world_history:5 | 0|acc |0.7046|± |0.0297|
|leaderboard:mmlu:human_aging:5 | 0|acc |0.6323|± |0.0324|
|leaderboard:mmlu:human_sexuality:5 | 0|acc |0.5878|± |0.0432|
|leaderboard:mmlu:international_law:5 | 0|acc |0.6694|± |0.0429|
|leaderboard:mmlu:jurisprudence:5 | 0|acc |0.7037|± |0.0441|
|leaderboard:mmlu:logical_fallacies:5 | 0|acc |0.6564|± |0.0373|
|leaderboard:mmlu:machine_learning:5 | 0|acc |0.3393|± |0.0449|
|leaderboard:mmlu:management:5 | 0|acc |0.7087|± |0.0450|
|leaderboard:mmlu:marketing:5 | 0|acc |0.8333|± |0.0244|
|leaderboard:mmlu:medical_genetics:5 | 0|acc |0.5400|± |0.0501|
|leaderboard:mmlu:miscellaneous:5 | 0|acc |0.7382|± |0.0157|
|leaderboard:mmlu:moral_disputes:5 | 0|acc |0.6127|± |0.0262|
|leaderboard:mmlu:moral_scenarios:5 | 0|acc |0.3788|± |0.0162|
|leaderboard:mmlu:nutrition:5 | 0|acc |0.6046|± |0.0280|
|leaderboard:mmlu:philosophy:5 | 0|acc |0.6270|± |0.0275|
|leaderboard:mmlu:prehistory:5 | 0|acc |0.6204|± |0.0270|
|leaderboard:mmlu:professional_accounting:5 | 0|acc |0.3582|± |0.0286|
|leaderboard:mmlu:professional_law:5 | 0|acc |0.3931|± |0.0125|
|leaderboard:mmlu:professional_medicine:5 | 0|acc |0.5184|± |0.0304|
|leaderboard:mmlu:professional_psychology:5 | 0|acc |0.5556|± |0.0201|
|leaderboard:mmlu:public_relations:5 | 0|acc |0.6818|± |0.0446|
|leaderboard:mmlu:security_studies:5 | 0|acc |0.6122|± |0.0312|
|leaderboard:mmlu:sociology:5 | 0|acc |0.7164|± |0.0319|
|leaderboard:mmlu:us_foreign_policy:5 | 0|acc |0.8200|± |0.0386|
|leaderboard:mmlu:virology:5 | 0|acc |0.4578|± |0.0388|
|leaderboard:mmlu:world_religions:5 | 0|acc |0.7661|± |0.0325|
|leaderboard:truthfulqa:mc:0 | 0|truthfulqa_mc1|0.2840|± |0.0158|
| | |truthfulqa_mc2|0.4423|± |0.0146|
|leaderboard:winogrande:5 | 0|acc |0.7593|± |0.0120|
## TC-Eval
| Task |Version|Metric|Value | |Stderr|
|---------------------------------------------------------------------------------|------:|------|-----:|---|-----:|
|community:tc-eval-v2:penguin_table:0| 0|acc |0.2361|± |0.0355|
|community:tc-eval-v2:_average:5 | |acc |0.3508|± |0.0318|
|community:tc-eval-v2:tmmluplus-accounting:5 | 0|acc |0.2565|± |0.0317|
|community:tc-eval-v2:tmmluplus-administrative_law:5 | 0|acc |0.2833|± |0.0220|
|community:tc-eval-v2:tmmluplus-advance_chemistry:5 | 0|acc |0.3333|± |0.0427|
|community:tc-eval-v2:tmmluplus-agriculture:5 | 0|acc |0.1987|± |0.0326|
|community:tc-eval-v2:tmmluplus-anti_money_laundering:5 | 0|acc |0.5597|± |0.0430|
|community:tc-eval-v2:tmmluplus-auditing:5 | 0|acc |0.2836|± |0.0192|
|community:tc-eval-v2:tmmluplus-basic_medical_science:5 | 0|acc |0.2841|± |0.0146|
|community:tc-eval-v2:tmmluplus-business_management:5 | 0|acc |0.4245|± |0.0421|
|community:tc-eval-v2:tmmluplus-chinese_language_and_literature:5 | 0|acc |0.2714|± |0.0316|
|community:tc-eval-v2:tmmluplus-clinical_psychology:5 | 0|acc |0.3840|± |0.0437|
|community:tc-eval-v2:tmmluplus-computer_science:5 | 0|acc |0.4195|± |0.0375|
|community:tc-eval-v2:tmmluplus-culinary_skills:5 | 0|acc |0.4589|± |0.0292|
|community:tc-eval-v2:tmmluplus-dentistry:5 | 0|acc |0.3885|± |0.0244|
|community:tc-eval-v2:tmmluplus-economics:5 | 0|acc |0.3053|± |0.0233|
|community:tc-eval-v2:tmmluplus-education:5 | 0|acc |0.4355|± |0.0447|
|community:tc-eval-v2:tmmluplus-education_(profession_level):5 | 0|acc |0.2819|± |0.0204|
|community:tc-eval-v2:tmmluplus-educational_psychology:5 | 0|acc |0.4489|± |0.0376|
|community:tc-eval-v2:tmmluplus-engineering_math:5 | 0|acc |0.2718|± |0.0441|
|community:tc-eval-v2:tmmluplus-finance_banking:5 | 0|acc |0.3037|± |0.0397|
|community:tc-eval-v2:tmmluplus-financial_analysis:5 | 0|acc |0.2801|± |0.0230|
|community:tc-eval-v2:tmmluplus-fire_science:5 | 0|acc |0.2500|± |0.0390|
|community:tc-eval-v2:tmmluplus-general_principles_of_law:5 | 0|acc |0.3113|± |0.0452|
|community:tc-eval-v2:tmmluplus-geography_of_taiwan:5 | 0|acc |0.4492|± |0.0180|
|community:tc-eval-v2:tmmluplus-human_behavior:5 | 0|acc |0.3883|± |0.0278|
|community:tc-eval-v2:tmmluplus-insurance_studies:5 | 0|acc |0.3487|± |0.0173|
|community:tc-eval-v2:tmmluplus-introduction_to_law:5 | 0|acc |0.3165|± |0.0303|
|community:tc-eval-v2:tmmluplus-jce_humanities:5 | 0|acc |0.3444|± |0.0504|
|community:tc-eval-v2:tmmluplus-junior_chemistry:5 | 0|acc |0.3158|± |0.0322|
|community:tc-eval-v2:tmmluplus-junior_chinese_exam:5 | 0|acc |0.4171|± |0.0374|
|community:tc-eval-v2:tmmluplus-junior_math_exam:5 | 0|acc |0.2286|± |0.0318|
|community:tc-eval-v2:tmmluplus-junior_science_exam:5 | 0|acc |0.3427|± |0.0326|
|community:tc-eval-v2:tmmluplus-junior_social_studies:5 | 0|acc |0.4683|± |0.0446|
|community:tc-eval-v2:tmmluplus-logic_reasoning:5 | 0|acc |0.2734|± |0.0379|
|community:tc-eval-v2:tmmluplus-macroeconomics:5 | 0|acc |0.3187|± |0.0230|
|community:tc-eval-v2:tmmluplus-management_accounting:5 | 0|acc |0.2977|± |0.0313|
|community:tc-eval-v2:tmmluplus-marketing_management:5 | 0|acc |0.4624|± |0.0520|
|community:tc-eval-v2:tmmluplus-mechanical:5 | 0|acc |0.4831|± |0.0462|
|community:tc-eval-v2:tmmluplus-music:5 | 0|acc |0.3993|± |0.0294|
|community:tc-eval-v2:tmmluplus-national_protection:5 | 0|acc |0.4929|± |0.0345|
|community:tc-eval-v2:tmmluplus-nautical_science:5 | 0|acc |0.2777|± |0.0191|
|community:tc-eval-v2:tmmluplus-occupational_therapy_for_psychological_disorders:5| 0|acc |0.4438|± |0.0213|
|community:tc-eval-v2:tmmluplus-official_document_management:5 | 0|acc |0.3559|± |0.0322|
|community:tc-eval-v2:tmmluplus-optometry:5 | 0|acc |0.2804|± |0.0148|
|community:tc-eval-v2:tmmluplus-organic_chemistry:5 | 0|acc |0.3486|± |0.0459|
|community:tc-eval-v2:tmmluplus-pharmacology:5 | 0|acc |0.3397|± |0.0197|
|community:tc-eval-v2:tmmluplus-pharmacy:5 | 0|acc |0.2174|± |0.0209|
|community:tc-eval-v2:tmmluplus-physical_education:5 | 0|acc |0.3966|± |0.0367|
|community:tc-eval-v2:tmmluplus-physics:5 | 0|acc |0.2371|± |0.0434|
|community:tc-eval-v2:tmmluplus-politic_science:5 | 0|acc |0.3407|± |0.0150|
|community:tc-eval-v2:tmmluplus-real_estate:5 | 0|acc |0.3804|± |0.0509|
|community:tc-eval-v2:tmmluplus-secondary_physics:5 | 0|acc |0.3393|± |0.0449|
|community:tc-eval-v2:tmmluplus-statistics_and_machine_learning:5 | 0|acc |0.3438|± |0.0318|
|community:tc-eval-v2:tmmluplus-taiwanese_hokkien:5 | 0|acc |0.2636|± |0.0389|
|community:tc-eval-v2:tmmluplus-taxation:5 | 0|acc |0.2507|± |0.0224|
|community:tc-eval-v2:tmmluplus-technical:5 | 0|acc |0.4204|± |0.0247|
|community:tc-eval-v2:tmmluplus-three_principles_of_people:5 | 0|acc |0.5396|± |0.0424|
|community:tc-eval-v2:tmmluplus-trade:5 | 0|acc |0.2251|± |0.0187|
|community:tc-eval-v2:tmmluplus-traditional_chinese_medicine_clinical_medicine:5 | 0|acc |0.3094|± |0.0278|
|community:tc-eval-v2:tmmluplus-trust_practice:5 | 0|acc |0.3292|± |0.0235|
|community:tc-eval-v2:tmmluplus-ttqav2:5 | 0|acc |0.6726|± |0.0443|
|community:tc-eval-v2:tmmluplus-tve_chinese_language:5 | 0|acc |0.4161|± |0.0225|
|community:tc-eval-v2:tmmluplus-tve_design:5 | 0|acc |0.4542|± |0.0227|
|community:tc-eval-v2:tmmluplus-tve_mathematics:5 | 0|acc |0.2733|± |0.0365|
|community:tc-eval-v2:tmmluplus-tve_natural_sciences:5 | 0|acc |0.3349|± |0.0229|
|community:tc-eval-v2:tmmluplus-veterinary_pathology:5 | 0|acc |0.2544|± |0.0259|
|community:tc-eval-v2:tmmluplus-veterinary_pharmacology:5 | 0|acc |0.3259|± |0.0202| |