CaiRou-Huang/copyTwLLM

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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: libraryname: transformers pipelinetag: text-generation extragatedheading: Acknowledge license to accept the repository. extragatedprompt: Please contact the author for access. extragatedbuttoncontent: Acknowledge license 同意以上內容 extragated_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 --- <img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

🌟 Checkout Taiwan-LLM Demo Chat-UI 🌟

Model Card for Taiwan LLM 7B v2.1 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 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: yentinglin/Taiwan-LLM-7B-v2.0-base

Model Sources

<!-- Provide the basic links for the model. -->

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

Performance

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-7B-v2.1-chat", 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

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
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.03
• —num_epochs: 5.0

Citation

If you find Taiwan LLM is 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

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