flashcardsGPT-Qwen2-7B-v0.1-GGUF
- This model is a fine-tuned version of unsloth/Qwen2-7b on an dataset created by Valerio Job based on real university lecture data.
- Version 0.1 of flashcardsGPT has only been trained on the module "Time Series Analysis with R" which is part of the BSc Business-IT programme offered by the FHNW university (more info).
- This repo includes the quantized models in the GGUF format. There is a separate repo called valeriojob/flashcardsGPT-Qwen2-7B-v0.1 that includes the default format of the model as well as the LoRA adapters of the model.
- This model was quantized using llama.cpp.
Model description
This model takes the OCR-extracted text from a university lecture slide as an input. It then generates high quality flashcards and returns them as a JSON object. It uses the following Prompt Engineering template:
""" Your task is to process the below OCR-extracted text from university lecture slides and create a set of flashcards with the key information about the topic. Format the flashcards as a JSON object, with each card having a 'front' field for the question or term, and a 'back' field for the corresponding answer or definition, which may include a short example. Ensure the 'back' field contains no line breaks. No additional text or explanation should be provided—only respond with the JSON object.
Here is the OCR-extracted text: """"
Intended uses & limitations
The fine-tuned model can be used to generate high-quality flashcards based on TSAR lectures from the BSc BIT programme offered by the FHNW university.
Training and evaluation data
The dataset (train and test) used for fine-tuning this model can be found here: datasets/valeriojob/FHNW-Flashcards-Data-v0.1
Licenses
- License: apache-2.0
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