Create README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+---
+<p align="center">
+<img src="https://github.com/lbh0830/TW-Hokkien-LLM/blob/main/pics/logo.jpg?raw=true" alt="Taigi-llama-logo" width="350">
+</p>
+
+# Model Card for Taigi-Llama-2-13B
+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.
+
+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)
+
+Explore other models and datasets in the [Taiwanese Hokkien LLM collection](https://huggingface.co/collections/Bohanlu/taiwanese-hokkien-llm-6614ba7456e6789bc2f10ca0).
+
+## Model description
+
+- **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.
+- **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.
+- **Input:** Text
+- **Output:** Text
+- **Model Size:** 13B parameters
+
+## Usage Example
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline
+import torch
+import accelerate
+
+def get_pipeline(path:str, tokenizer:AutoTokenizer, accelerator:accelerate.Accelerator) -> TextGenerationPipeline:
+    model = AutoModelForCausalLM.from_pretrained(
+        path, torch_dtype=torch.float16, device_map='auto', trust_remote_code=True)
+    
+    terminators = [tokenizer.eos_token_id, tokenizer.pad_token_id]
+
+    pipeline = TextGenerationPipeline(model = model, tokenizer = tokenizer, num_workers=accelerator.state.num_processes*4, pad_token_id=tokenizer.pad_token_id, eos_token_id=terminators)
+
+    return pipeline
+
+model_dir = "Bohanlu/Taigi-Llama-2-7B" # or Bohanlu/Taigi-Llama-2-13B for the 13B model
+tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False)
+
+accelerator = accelerate.Accelerator()
+pipe = get_pipeline(model_dir, tokenizer, accelerator)
+
+# Few-shot示例：問答
+qa_prompt = """Example 1:
+問題：台北101有偌懸？
+答案：台北101的高度是五百空八公尺。
+
+Example 2:
+問題：台灣上長的溪仔是佗一條？
+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。
+
+Example 3:
+問題：臺灣上懸的山是啥物？
+答案："""
+
+print(pipe(qa_prompt, return_full_text=False))
+>>> [{'generated_text': '臺灣上懸的山是玉山，海拔三千九百五十二公尺。'}]
+```
+
+## Citation
+
+If you find the resources in the Taiwanese Hokkien LLM collection useful in your work, please cite it using the following reference:
+
+```
+@misc{lu2024enhancing,
+      title={Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems}, 
+      author={Bo-Han Lu and Yi-Hsuan Lin and En-Shiun Annie Lee and Richard Tzong-Han Tsai},
+      year={2024},
+      eprint={2403.12024},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```