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Gemma-2B Quiz Answering Model

This project fine-tunes the Gemma-2B model to provide answers to quiz-related questions. The model is designed to handle complex problems or quizzes and generate clear and accurate responses in Korean.

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Model Overview

The Gemma-2B Quiz Answering Model is built on top of the Gemma-2B base model. This version has been fine-tuned to better handle complex quiz questions and generate responses in natural Korean, addressing issues with awkward language generation from the base model.

  • Model Name: gemma-2b-quiz-ko
  • Purpose: Answer complex quiz and problem-solving questions.
  • Language: Korean (ko)

How to Use

You can use the model by loading it from Hugging Face Hub. Below is a simple usage example with the transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("DORAEMONG/gemma-2b-quiz-ko")
tokenizer = AutoTokenizer.from_pretrained("DORAEMONG/gemma-2b-quiz-ko")

# Input a quiz question
question = "λ‹€μŒ μˆ˜ν•™ 문제의 닡은 λ¬΄μ—‡μž…λ‹ˆκΉŒ? μŠ€ν”Όλ„ˆκ°€ A, B, C둜 λ‚˜λ‰˜μ–΄ μžˆμ„ λ•Œ..."

inputs = tokenizer(question, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)

# Decode the generated text
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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Dataset used to train DORAEMONG/gemma-2b-it-quiz-ko