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MinKeonKim/
gemma-2b-it-quiz-ko

Name: gemma-2b-it-quiz-ko
Availability: InStock

safetensors

text2text-generation

MarkrAI/KOpen-HQ-Hermes-2.5-60K

google/gemma-2-2b-it

finetune

gemma

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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.

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:

python

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))

MinKeonKim/gemma-2b-it-quiz-ko

Gemma-2B Quiz Answering Model

Table of Contents

Model Overview

How to Use

MinKeonKim/
gemma-2b-it-quiz-ko