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--- |
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license: llama2 |
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datasets: |
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- yentinglin/traditional_mandarin_instructions |
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language: |
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- zh |
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widget: |
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- 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:" |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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<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'"/> |
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# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟 |
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# Model Card for Taiwan LLM 13B v0.0 chat |
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Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan. |
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Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning. |
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This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances. |
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It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance. |
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For detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf). |
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## Model description |
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- **Model type:** A 13B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. |
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- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw) |
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- **Finetuned from model:** [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa |
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- **Demo:** https://twllm.com/ |
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## Performance |
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## Intended uses |
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Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: |
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```python |
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# pip install transformers>=4.34 |
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# pip install accelerate |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="yentinglin/Taiwan-LLaMa-v0.0", torch_dtype=torch.bfloat16, device_map="auto") |
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# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [ |
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{ |
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"role": "system", |
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"content": "你是一個人工智慧助理", |
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}, |
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{"role": "user", "content": "東北季風如何影響台灣氣候？"}, |
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] |
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- distributed_type: multi-GPU |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 5.0 |
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## Citation |
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If you find Taiwan LLM is useful in your work, please cite it with: |
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``` |
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@misc{lin2023taiwan, |
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title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model}, |
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author={Yen-Ting Lin and Yun-Nung Chen}, |
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year={2023}, |
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eprint={2311.17487}, |
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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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