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--- |
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license: gemma |
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datasets: |
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- MarkrAI/KOpen-HQ-Hermes-2.5-60K |
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language: |
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- ko |
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metrics: |
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- accuracy |
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base_model: |
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- google/gemma-2-2b-it |
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pipeline_tag: text2text-generation |
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--- |
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# Gemma-2B Quiz Answering Model |
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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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## Table of Contents |
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- [Model Overview](#model-overview) |
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- [How to Use](#how-to-use) |
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- [Training Details](#training-details) |
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- [Model Performance](#model-performance) |
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- [Limitations and Future Work](#limitations-and-future-work) |
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## Model Overview |
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The **Gemma-2B Quiz Answering Model** is built on top of the [Gemma-2B](https://huggingface.co/google/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. |
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- **Model Name**: `gemma-2b-quiz-ko` |
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- **Purpose**: Answer complex quiz and problem-solving questions. |
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- **Language**: Korean (ko) |
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## How to Use |
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You can use the model by loading it from Hugging Face Hub. Below is a simple usage example with the `transformers` library: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained("DORAEMONG/gemma-2b-quiz-ko") |
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tokenizer = AutoTokenizer.from_pretrained("DORAEMONG/gemma-2b-quiz-ko") |
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# Input a quiz question |
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question = "λ€μ μν λ¬Έμ μ λ΅μ 무μμ
λκΉ? μ€νΌλκ° A, B, Cλ‘ λλμ΄ μμ λ..." |
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inputs = tokenizer(question, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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# Decode the generated text |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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