76 lines
3.2 KiB
Markdown
76 lines
3.2 KiB
Markdown
---
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license: cc-by-nc-sa-4.0
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---
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<p align="center">
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<img src="https://github.com/lbh0830/TW-Hokkien-LLM/blob/main/pics/logo.jpg?raw=true" alt="Taigi-llama-logo" width="350">
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</p>
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# Model Card for Taigi-Llama-2-7B
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The Taigi-Llama-2 series are built based on the Traditional Chinese version of the LLaMA-2 model. We conducted continued pre-training on web-scraped data in Taiwanese Hokkien, including Hanzi, POJ, and Hanlo, totaling around 78MB.
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For more details, please refer to our [GitHub repository](https://github.com/lbh0830/TW-Hokkien-LLM/tree/main) and the paper: [Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems](https://arxiv.org/abs/2403.12024)
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Explore other models and datasets in the [Taiwanese Hokkien LLM collection](https://huggingface.co/collections/Bohanlu/taiwanese-hokkien-llm-6614ba7456e6789bc2f10ca0).
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## Model description
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- **Usage:** This model can be used for causal language modeling tasks in Taiwanese Hokkien. It is also suitable for further fine-tuning on specific datasets for downstream tasks.
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- **Language(s) (NLP):** The primary language is Taiwanese Hokkien (Hanzi and POJ). The model also retains capabilities in English and Mandarin Chinese due to prior pre-training.
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- **Input:** Text
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- **Output:** Text
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- **Model Size:** 7B parameters
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## Usage Example
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline
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import torch
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import accelerate
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def get_pipeline(path:str, tokenizer:AutoTokenizer, accelerator:accelerate.Accelerator) -> TextGenerationPipeline:
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model = AutoModelForCausalLM.from_pretrained(
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path, torch_dtype=torch.float16, device_map='auto', trust_remote_code=True)
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terminators = [tokenizer.eos_token_id, tokenizer.pad_token_id]
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pipeline = TextGenerationPipeline(model = model, tokenizer = tokenizer, num_workers=accelerator.state.num_processes*4, pad_token_id=tokenizer.pad_token_id, eos_token_id=terminators)
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return pipeline
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model_dir = "Bohanlu/Taigi-Llama-2-7B" # or Bohanlu/Taigi-Llama-2-13B for the 13B model
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tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False)
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accelerator = accelerate.Accelerator()
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pipe = get_pipeline(model_dir, tokenizer, accelerator)
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# Few-shot示例：問答
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qa_prompt = """Example 1:
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問題：台北101有偌懸？
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答案：台北101的高度是五百空八公尺。
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Example 2:
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問題：台灣上長的溪仔是佗一條？
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答案：台灣上長的溪仔是濁水溪，規个長度有百八公里遐爾長。
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Example 3:
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問題：臺灣上懸的山是啥物？
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答案："""
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print(pipe(qa_prompt, return_full_text=False))
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# Output: [{'generated_text': '臺灣上懸的山是玉山，海拔三千九百五十二公尺。'}]
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```
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## Citation
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If you find the resources in the Taiwanese Hokkien LLM collection useful in your work, please cite it using the following reference:
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```
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@misc{lu2024enhancing,
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title={Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems},
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author={Bo-Han Lu and Yi-Hsuan Lin and En-Shiun Annie Lee and Richard Tzong-Han Tsai},
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year={2024},
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eprint={2403.12024},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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