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--- |
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license: apache-2.0 |
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language: |
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- zh |
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widget: |
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- text: >- |
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A chat between a curious user and an artificial intelligence assistant. |
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The assistant gives helpful, detailed, and polite answers to the user's |
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questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT: |
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library_name: transformers |
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pipeline_tag: text-generation |
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extra_gated_heading: Acknowledge license to accept the repository. |
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extra_gated_prompt: Please contact the author for access. |
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extra_gated_button_content: Acknowledge license 同意以上內容 |
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extra_gated_fields: |
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Name: text |
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Mail: text |
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Organization: text |
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Country: text |
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Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox |
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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 |
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--- |
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<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'"/> |
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# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟 |
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# Model Card for Taiwan LLM 13B v2.0 chat |
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Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan. |
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Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning. |
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This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances. |
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It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance. |
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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). |
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## Model description |
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- **Model type:** A 13B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. |
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- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw) |
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- **Finetuned from model:** [yentinglin/Taiwan-LLM-13B-v2.0-base](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-base) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa |
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- **Demo:** https://twllm.com/ |
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## Performance |
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TMMLUS+ score: 24.76727075757576 |
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## Intended uses |
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Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: |
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```python |
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# pip install transformers>=4.34 |
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# pip install accelerate |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="yentinglin/Taiwan-LLM-13B-v2.0-chat", torch_dtype=torch.bfloat16, device_map="auto") |
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# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [ |
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{ |
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"role": "system", |
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"content": "你是一個人工智慧助理", |
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}, |
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{"role": "user", "content": "東北季風如何影響台灣氣候？"}, |
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] |
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- distributed_type: multi-GPU |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 5.0 |
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## Citation |
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If you find Taiwan LLM is useful in your work, please cite it with: |
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``` |
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@misc{lin2023taiwan, |
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title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model}, |
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author={Yen-Ting Lin and Yun-Nung Chen}, |
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year={2023}, |
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eprint={2311.17487}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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# Acknowledgement |
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Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project. |
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## Open LLM Leaderboard |
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| Task |Version| Metric |Value | |Stderr| |
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|------------------------------------------------------|------:|--------------|-----:|---|-----:| |
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|leaderboard:arc:challenge:25 | 0|acc |0.5529|± |0.0145| |
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| | |acc_norm |0.5862|± |0.0144| |
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|leaderboard:gsm8k:5 | 0|qem |0.3177|± |0.0128| |
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|leaderboard:hellaswag:10 | 0|acc |0.6307|± |0.0048| |
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| | |acc_norm |0.8327|± |0.0037| |
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|leaderboard:mmlu:_average:5 | |acc |0.5483|± |0.0356| |
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|leaderboard:mmlu:abstract_algebra:5 | 0|acc |0.3400|± |0.0476| |
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|leaderboard:mmlu:anatomy:5 | 0|acc |0.5111|± |0.0432| |
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|leaderboard:mmlu:astronomy:5 | 0|acc |0.5789|± |0.0402| |
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|leaderboard:mmlu:business_ethics:5 | 0|acc |0.5100|± |0.0502| |
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|leaderboard:mmlu:clinical_knowledge:5 | 0|acc |0.6000|± |0.0302| |
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|leaderboard:mmlu:college_biology:5 | 0|acc |0.5764|± |0.0413| |
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|leaderboard:mmlu:college_chemistry:5 | 0|acc |0.4100|± |0.0494| |
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|leaderboard:mmlu:college_computer_science:5 | 0|acc |0.4500|± |0.0500| |
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|leaderboard:mmlu:college_mathematics:5 | 0|acc |0.3800|± |0.0488| |
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|leaderboard:mmlu:college_medicine:5 | 0|acc |0.5434|± |0.0380| |
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|leaderboard:mmlu:college_physics:5 | 0|acc |0.2941|± |0.0453| |
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|leaderboard:mmlu:computer_security:5 | 0|acc |0.7000|± |0.0461| |
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|leaderboard:mmlu:conceptual_physics:5 | 0|acc |0.4468|± |0.0325| |
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|leaderboard:mmlu:econometrics:5 | 0|acc |0.2719|± |0.0419| |
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|leaderboard:mmlu:electrical_engineering:5 | 0|acc |0.4552|± |0.0415| |
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|leaderboard:mmlu:elementary_mathematics:5 | 0|acc |0.3175|± |0.0240| |
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|leaderboard:mmlu:formal_logic:5 | 0|acc |0.3413|± |0.0424| |
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|leaderboard:mmlu:global_facts:5 | 0|acc |0.3700|± |0.0485| |
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|leaderboard:mmlu:high_school_biology:5 | 0|acc |0.6323|± |0.0274| |
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|leaderboard:mmlu:high_school_chemistry:5 | 0|acc |0.4581|± |0.0351| |
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|leaderboard:mmlu:high_school_computer_science:5 | 0|acc |0.5400|± |0.0501| |
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|leaderboard:mmlu:high_school_european_history:5 | 0|acc |0.6364|± |0.0376| |
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|leaderboard:mmlu:high_school_geography:5 | 0|acc |0.6970|± |0.0327| |
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|leaderboard:mmlu:high_school_government_and_politics:5| 0|acc |0.7617|± |0.0307| |
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|leaderboard:mmlu:high_school_macroeconomics:5 | 0|acc |0.4974|± |0.0254| |
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|leaderboard:mmlu:high_school_mathematics:5 | 0|acc |0.3296|± |0.0287| |
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|leaderboard:mmlu:high_school_microeconomics:5 | 0|acc |0.5336|± |0.0324| |
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|leaderboard:mmlu:high_school_physics:5 | 0|acc |0.3709|± |0.0394| |
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|leaderboard:mmlu:high_school_psychology:5 | 0|acc |0.7468|± |0.0186| |
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|leaderboard:mmlu:high_school_statistics:5 | 0|acc |0.4074|± |0.0335| |
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|leaderboard:mmlu:high_school_us_history:5 | 0|acc |0.7108|± |0.0318| |
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|leaderboard:mmlu:high_school_world_history:5 | 0|acc |0.7046|± |0.0297| |
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|leaderboard:mmlu:human_aging:5 | 0|acc |0.6323|± |0.0324| |
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|leaderboard:mmlu:human_sexuality:5 | 0|acc |0.5878|± |0.0432| |
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|leaderboard:mmlu:international_law:5 | 0|acc |0.6694|± |0.0429| |
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|leaderboard:mmlu:jurisprudence:5 | 0|acc |0.7037|± |0.0441| |
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|leaderboard:mmlu:logical_fallacies:5 | 0|acc |0.6564|± |0.0373| |
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|leaderboard:mmlu:machine_learning:5 | 0|acc |0.3393|± |0.0449| |
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|leaderboard:mmlu:management:5 | 0|acc |0.7087|± |0.0450| |
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|leaderboard:mmlu:marketing:5 | 0|acc |0.8333|± |0.0244| |
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|leaderboard:mmlu:medical_genetics:5 | 0|acc |0.5400|± |0.0501| |
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|leaderboard:mmlu:miscellaneous:5 | 0|acc |0.7382|± |0.0157| |
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|leaderboard:mmlu:moral_disputes:5 | 0|acc |0.6127|± |0.0262| |
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|leaderboard:mmlu:moral_scenarios:5 | 0|acc |0.3788|± |0.0162| |
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|leaderboard:mmlu:nutrition:5 | 0|acc |0.6046|± |0.0280| |
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|leaderboard:mmlu:philosophy:5 | 0|acc |0.6270|± |0.0275| |
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|leaderboard:mmlu:prehistory:5 | 0|acc |0.6204|± |0.0270| |
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|leaderboard:mmlu:professional_accounting:5 | 0|acc |0.3582|± |0.0286| |
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|leaderboard:mmlu:professional_law:5 | 0|acc |0.3931|± |0.0125| |
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|leaderboard:mmlu:professional_medicine:5 | 0|acc |0.5184|± |0.0304| |
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|leaderboard:mmlu:professional_psychology:5 | 0|acc |0.5556|± |0.0201| |
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|leaderboard:mmlu:public_relations:5 | 0|acc |0.6818|± |0.0446| |
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|leaderboard:mmlu:security_studies:5 | 0|acc |0.6122|± |0.0312| |
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|leaderboard:mmlu:sociology:5 | 0|acc |0.7164|± |0.0319| |
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|leaderboard:mmlu:us_foreign_policy:5 | 0|acc |0.8200|± |0.0386| |
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|leaderboard:mmlu:virology:5 | 0|acc |0.4578|± |0.0388| |
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|leaderboard:mmlu:world_religions:5 | 0|acc |0.7661|± |0.0325| |
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|leaderboard:truthfulqa:mc:0 | 0|truthfulqa_mc1|0.2840|± |0.0158| |
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| | |truthfulqa_mc2|0.4423|± |0.0146| |
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|leaderboard:winogrande:5 | 0|acc |0.7593|± |0.0120| |
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## TC-Eval |
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| Task |Version|Metric|Value | |Stderr| |
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| Task |Version|Metric|Value | |Stderr| |
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|community:tc-eval-v2:drcd:0| 0|pem |0.6848|± |0.0079| |
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| | |pqem |0.6799|± |0.0079| |
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|community:tc-eval-v2:penguin_table:0| 0|acc |0.2361|± |0.0355| |
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|community:tc-eval-v2:_average:5 | |acc |0.3508|± |0.0318| |
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|community:tc-eval-v2:tmmluplus-accounting:5 | 0|acc |0.2565|± |0.0317| |
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|community:tc-eval-v2:tmmluplus-administrative_law:5 | 0|acc |0.2833|± |0.0220| |
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|community:tc-eval-v2:tmmluplus-advance_chemistry:5 | 0|acc |0.3333|± |0.0427| |
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|community:tc-eval-v2:tmmluplus-agriculture:5 | 0|acc |0.1987|± |0.0326| |
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|community:tc-eval-v2:tmmluplus-anti_money_laundering:5 | 0|acc |0.5597|± |0.0430| |
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|community:tc-eval-v2:tmmluplus-auditing:5 | 0|acc |0.2836|± |0.0192| |
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|community:tc-eval-v2:tmmluplus-basic_medical_science:5 | 0|acc |0.2841|± |0.0146| |
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|community:tc-eval-v2:tmmluplus-business_management:5 | 0|acc |0.4245|± |0.0421| |
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|community:tc-eval-v2:tmmluplus-chinese_language_and_literature:5 | 0|acc |0.2714|± |0.0316| |
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|community:tc-eval-v2:tmmluplus-clinical_psychology:5 | 0|acc |0.3840|± |0.0437| |
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|community:tc-eval-v2:tmmluplus-computer_science:5 | 0|acc |0.4195|± |0.0375| |
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|community:tc-eval-v2:tmmluplus-culinary_skills:5 | 0|acc |0.4589|± |0.0292| |
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|community:tc-eval-v2:tmmluplus-dentistry:5 | 0|acc |0.3885|± |0.0244| |
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|community:tc-eval-v2:tmmluplus-economics:5 | 0|acc |0.3053|± |0.0233| |
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|community:tc-eval-v2:tmmluplus-education:5 | 0|acc |0.4355|± |0.0447| |
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|community:tc-eval-v2:tmmluplus-education_(profession_level):5 | 0|acc |0.2819|± |0.0204| |
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|community:tc-eval-v2:tmmluplus-educational_psychology:5 | 0|acc |0.4489|± |0.0376| |
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|community:tc-eval-v2:tmmluplus-engineering_math:5 | 0|acc |0.2718|± |0.0441| |
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|community:tc-eval-v2:tmmluplus-finance_banking:5 | 0|acc |0.3037|± |0.0397| |
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|community:tc-eval-v2:tmmluplus-financial_analysis:5 | 0|acc |0.2801|± |0.0230| |
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|community:tc-eval-v2:tmmluplus-fire_science:5 | 0|acc |0.2500|± |0.0390| |
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|community:tc-eval-v2:tmmluplus-general_principles_of_law:5 | 0|acc |0.3113|± |0.0452| |
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|community:tc-eval-v2:tmmluplus-geography_of_taiwan:5 | 0|acc |0.4492|± |0.0180| |
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|community:tc-eval-v2:tmmluplus-human_behavior:5 | 0|acc |0.3883|± |0.0278| |
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|community:tc-eval-v2:tmmluplus-insurance_studies:5 | 0|acc |0.3487|± |0.0173| |
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|community:tc-eval-v2:tmmluplus-introduction_to_law:5 | 0|acc |0.3165|± |0.0303| |
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|community:tc-eval-v2:tmmluplus-jce_humanities:5 | 0|acc |0.3444|± |0.0504| |
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|community:tc-eval-v2:tmmluplus-junior_chemistry:5 | 0|acc |0.3158|± |0.0322| |
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|community:tc-eval-v2:tmmluplus-junior_chinese_exam:5 | 0|acc |0.4171|± |0.0374| |
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|community:tc-eval-v2:tmmluplus-junior_math_exam:5 | 0|acc |0.2286|± |0.0318| |
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|community:tc-eval-v2:tmmluplus-junior_science_exam:5 | 0|acc |0.3427|± |0.0326| |
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|community:tc-eval-v2:tmmluplus-junior_social_studies:5 | 0|acc |0.4683|± |0.0446| |
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|community:tc-eval-v2:tmmluplus-logic_reasoning:5 | 0|acc |0.2734|± |0.0379| |
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|community:tc-eval-v2:tmmluplus-macroeconomics:5 | 0|acc |0.3187|± |0.0230| |
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|community:tc-eval-v2:tmmluplus-management_accounting:5 | 0|acc |0.2977|± |0.0313| |
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|community:tc-eval-v2:tmmluplus-marketing_management:5 | 0|acc |0.4624|± |0.0520| |
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|community:tc-eval-v2:tmmluplus-mechanical:5 | 0|acc |0.4831|± |0.0462| |
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|community:tc-eval-v2:tmmluplus-music:5 | 0|acc |0.3993|± |0.0294| |
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|community:tc-eval-v2:tmmluplus-national_protection:5 | 0|acc |0.4929|± |0.0345| |
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|community:tc-eval-v2:tmmluplus-nautical_science:5 | 0|acc |0.2777|± |0.0191| |
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|community:tc-eval-v2:tmmluplus-occupational_therapy_for_psychological_disorders:5| 0|acc |0.4438|± |0.0213| |
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|community:tc-eval-v2:tmmluplus-official_document_management:5 | 0|acc |0.3559|± |0.0322| |
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|community:tc-eval-v2:tmmluplus-optometry:5 | 0|acc |0.2804|± |0.0148| |
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|community:tc-eval-v2:tmmluplus-organic_chemistry:5 | 0|acc |0.3486|± |0.0459| |
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|community:tc-eval-v2:tmmluplus-pharmacology:5 | 0|acc |0.3397|± |0.0197| |
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|community:tc-eval-v2:tmmluplus-pharmacy:5 | 0|acc |0.2174|± |0.0209| |
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|community:tc-eval-v2:tmmluplus-physical_education:5 | 0|acc |0.3966|± |0.0367| |
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|community:tc-eval-v2:tmmluplus-physics:5 | 0|acc |0.2371|± |0.0434| |
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|community:tc-eval-v2:tmmluplus-politic_science:5 | 0|acc |0.3407|± |0.0150| |
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|community:tc-eval-v2:tmmluplus-real_estate:5 | 0|acc |0.3804|± |0.0509| |
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|community:tc-eval-v2:tmmluplus-secondary_physics:5 | 0|acc |0.3393|± |0.0449| |
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|community:tc-eval-v2:tmmluplus-statistics_and_machine_learning:5 | 0|acc |0.3438|± |0.0318| |
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|community:tc-eval-v2:tmmluplus-taiwanese_hokkien:5 | 0|acc |0.2636|± |0.0389| |
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|community:tc-eval-v2:tmmluplus-taxation:5 | 0|acc |0.2507|± |0.0224| |
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|community:tc-eval-v2:tmmluplus-technical:5 | 0|acc |0.4204|± |0.0247| |
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|community:tc-eval-v2:tmmluplus-three_principles_of_people:5 | 0|acc |0.5396|± |0.0424| |
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|community:tc-eval-v2:tmmluplus-trade:5 | 0|acc |0.2251|± |0.0187| |
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|community:tc-eval-v2:tmmluplus-traditional_chinese_medicine_clinical_medicine:5 | 0|acc |0.3094|± |0.0278| |
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|community:tc-eval-v2:tmmluplus-trust_practice:5 | 0|acc |0.3292|± |0.0235| |
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|community:tc-eval-v2:tmmluplus-ttqav2:5 | 0|acc |0.6726|± |0.0443| |
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|community:tc-eval-v2:tmmluplus-tve_chinese_language:5 | 0|acc |0.4161|± |0.0225| |
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|community:tc-eval-v2:tmmluplus-tve_design:5 | 0|acc |0.4542|± |0.0227| |
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|community:tc-eval-v2:tmmluplus-tve_mathematics:5 | 0|acc |0.2733|± |0.0365| |
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|community:tc-eval-v2:tmmluplus-tve_natural_sciences:5 | 0|acc |0.3349|± |0.0229| |
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|community:tc-eval-v2:tmmluplus-veterinary_pathology:5 | 0|acc |0.2544|± |0.0259| |
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|community:tc-eval-v2:tmmluplus-veterinary_pharmacology:5 | 0|acc |0.3259|± |0.0202| |
