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.
Table of Contents
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 μν λ¬Έμ μ λ΅μ 무μμ
λκΉ? μ€νΌλκ° A, B, Cλ‘ λλμ΄ μμ λ..."
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))