You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Training procedure

Sci-MCQ-LLMs is a fine-tuned language model trained using the falcon-7b architecture. The model has been fine-tuned on a dataset of multiple-choice questions (MCQs) related to science subjects. The fine-tuning process was conducted using the Hugging Face Transformers library and supervised training techniques.

The fine-tuned model can generate predictions for science-related MCQs based on user input. It utilizes the 'falcon-7b' base model, which has a capacity of 7 billion parameters, making it suitable for complex language understanding tasks.

To use the Sci-MCQ-LLMs model, the user can provide a question or context, and the model will generate the most appropriate response among the available multiple-choice options. The predictions are generated through tokenization and language modeling techniques, ensuring accurate and contextually relevant answers.

Framework versions

PEFT 0.5.0.dev0

Downloads last month: -

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support