--- # 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 --- Taiwan LLM Logo # 🌟 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 - **Repository:** https://github.com/MiuLab/Taiwan-LLaMa - **Demo:** https://twllm.com/ ## Performance ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/HTwIzw6RDha2-PhuWqSuI.png) 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 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/MdvHwdUvH-c926qyRAw7K.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/kKpkvxDzOEyiAoTqmzRYO.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/FsnlJ_fkRxf7fn5RKZnjE.png) 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|