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Taiwan-LLaMa-v0.9

Text Generation
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PyTorch
Chinese
llama
text-generation-inference
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metadata
license: llama2
datasets:
  - yentinglin/zh_TW_c4
  - yentinglin/traditional_mandarin_instructions
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
Taiwan LLM Logo

🌟 Checkout Taiwan-LLM Demo Chat-UI 🌟

Model Card for Taiwan LLM 13B v0.9 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.

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-LLaMa-v1.0-base

Model Sources

Performance

image/png

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-LLaMa-v0.9", 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

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image/png

image/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:

@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},
}