metadata

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

🌟 Checkout Taiwan-LLM Demo Chat-UI 🌟

Model Card for Taiwan LLM 7B v2.0 base

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 7B 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: meta-llama/Llama-2-7b-hf

Model Sources

Repository: https://github.com/MiuLab/Taiwan-LLaMa
Demo: https://twllm.com/

Performance

Intended uses

You should fine-tuned this model for instruction-following / chat application.

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:

@inproceedings{lin-chen-2023-llm,
    title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models",
    author = "Lin, Yen-Ting  and Chen, Yun-Nung",
    booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.nlp4convai-1.5",
    pages = "47--58"
}

@misc{taiwanllama,
    author={Lin, Yen-Ting and Chen, Yun-Nung},
    title={Language Models for Taiwanese Culture},
    year={2023},
    url={https://github.com/MiuLab/Taiwan-LLaMa},
    note={Code and models available at https://github.com/MiuLab/Taiwan-LLaMa},
}

Acknowledgement

Taiwan LLM v2 is conducted in collaboration with Ubitus K.K.. Ubitus provides valuable compute resources for the project.