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:

python

# 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}
}

ZoneTwelve/Taiwan-LLM-8x7B-DPO-GGUF

🌟 Checkout Taiwan-LLM Demo Chat-UI 🌟

Model Card for Taiwan LLM 8x7B-DPO

Model description

Model Sources

Performance

Intended uses

Citation

ZoneTwelve/
Taiwan-LLM-8x7B-DPO-GGUF