The EEVE-Korean-Instruct-10.8B-v1.0 model is an advanced tool fine-tuned for generating polite and informative responses in Korean. This blog post will guide you through the entire process of utilizing this model effectively.

Getting Started

To use the EEVE-Korean-Instruct-10.8B-v1.0 model, start by installing the necessary libraries and following the prompt template provided below:

Installation

• Ensure you have Python and the Transformers library installed in your environment.
• Install via pip if you haven’t already:

  pip install transformers

Code Walkthrough

The following piece of code serves as a template to interact with the model:

from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-10.8B-v1.0")
tokenizer = AutoTokenizer.from_pretrained("yanolja/EEVE-Korean-Instruct-10.8B-v1.0")

prompt_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {prompt}\nAssistant:\n"

text = '한국의 수도는 어디인가요? 아래 선택지 중 골라주세요.\n\n(A) 경성\n(B) 부산\n(C) 평양\n(D) 서울\n(E) 전주'
model_inputs = tokenizer(prompt_template.format(prompt=text), return_tensors='pt')
outputs = model.generate(**model_inputs, max_new_tokens=256)
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(output_text)

This code functions like preparing a meal: You gather the ingredients (libraries), set the table (load the model), and then bake (run the prompt) to serve a delicious result (output).

Expected Output

When you run the code, you’ll receive a result similar to this:

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: 한국의 수도는 어디인가요? 아래 선택지 중 골라주세요.(A) 경성(B) 부산(C) 평양(D) 서울(E) 전주
Assistant: (D) 서울이 한국의 수도입니다. 서울은 나라의 북동부에 위치해 있으며, 정치, 경제, 문화의 중심지입니다. 약 1,000만 명이 넘는 인구를 가진 세계에서 가장 큰 도시 중 하나입니다. 서울은 높은 빌딩, 현대적인 인프라, 활기 문화 장면으로 유명합니다. 또한, 많은 역사적 명소와 박물관이 있어 방문객들에게 풍부한 문화 체험을 제공합니다.

Troubleshooting

If you encounter issues while using the model, consider the following troubleshooting tips:

• Ensure your Python environment is properly set up and that you have all the required libraries installed.
• Check if there are any typos in the model names; they must match exactly with the official Hugging Face repository.
• If the output is not as expected, try modifying the input prompt for clarity.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.