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 the 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

🌟 Checkout Taiwan-LLM Demo Chat-UI 🌟

Model Card for Taiwan LLM 8x7B-DPO

Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.

Model description

Model type: A 8x7B parameter Mixtral MoE 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-MoE-alpha

Model Sources

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

Performance

Checkout leaderboard in Tw Chatbot Arena

TMMLUS+ score:

yentinglin/Taiwan-LLM-MoE-alpha: 43.93
yentinglin/Taiwan-LLM-8x7B-DPO: TBD

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-8x7B-DPO", 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"])

Citation

If you find Taiwan LLM 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

Ubitus provides valuable compute resources for the project.