README.md

metadata

license: gemma
datasets:
  - MarkrAI/KOpen-HQ-Hermes-2.5-60K
language:
  - ko
metrics:
  - accuracy
base_model:
  - google/gemma-2-2b-it
pipeline_tag: text2text-generation

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
• How to Use
• Training Details
• Model Performance
• Limitations and Future Work

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 = "다음 수학 문제의 답은 무엇입니까? 스피너가 A, B, C로 나뉘어 있을 때..."

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))