Taiwan-LLM-8x7B-DPO

🌟 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

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• 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.

Disclaimer

This model is provided “as‑is” and without warranties of any kind. Users are solely responsible for evaluating the accuracy and suitability of the outputs. The developers assume no liability for any direct or indirect damages arising from its use.
The model is strictly not intended for high‑risk applications such as medical diagnosis, legal advice, or financial investment. For such use cases, please consult qualified professionals.

本模型「如是」（as‑is）提供，使用者須自行評估結果之正確性與適用性。開發者對於使用本模型所引發之任何直接或間接損失，不承擔任何法律責任。
嚴禁用於醫療診斷、法律諮詢、金融投資等高風險場景；若有相關需求，請尋求專業人員協助。