Taiwan LLM
Collection
https://twllm.com
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🌟 Checkout Taiwan-LLM Demo Chat-UI 🌟
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.
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
Model Sources
- 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:
# 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.. 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:drcd:0 | 0 | pem | 0.6848 | ± | 0.0079 |
| pqem | 0.6799 | ± | 0.0079 | ||
| 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 |
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