The fbellame/llama2-pdf-to-quizz-13b is a 13 billion parameter Llama 2-based causal language model, fine-tuned by fbellame using H2O LLM Studio. It is specifically optimized for generating multiple-choice questions (MCQs) with four options and a corresponding answer letter from provided document text. This model excels at creating detailed, context-specific quizzes, making it ideal for educational content generation and assessment tools.
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Model Overview
The fbellame/llama2-pdf-to-quizz-13b is a 13 billion parameter language model built upon the meta-llama/Llama-2-13b-hf architecture. It was fine-tuned by fbellame using H2O LLM Studio, specifically for the task of generating multiple-choice questions (MCQs) from given text documents.
Key Capabilities
- MCQ Generation: The model is trained to produce one multiple-choice question with four options (A, B, C, D) and a single-letter answer based on an input document.
- Contextual Questioning: Questions are designed to be detailed and derived solely from the information provided within the document, ensuring relevance and accuracy.
- Structured Output: It follows a specific output format for questions, choices, and answers, facilitating automated parsing and integration.
Training Details
The model was trained on a dataset of 168 prompts, each designed to generate a multiple-choice question and answer from a document. This specialized training dataset, available as fbellame/pdf_to_quizz_llama_13B on Hugging Face, focuses on the precise task of quiz creation.
Good For
- Educational Content Creation: Automating the generation of quizzes and assessment materials from textbooks, articles, or other documents.
- Knowledge Assessment: Quickly creating questions to test comprehension of specific textual content.
- Developers: Integrating into applications requiring automated question generation from PDFs or other text sources, particularly for educational technology platforms.