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--- |
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base_model: unsloth/Qwen2-7B |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- gguf |
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--- |
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# flashcardsGPT-Qwen2-7B-v0.1-GGUF |
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- This model is a fine-tuned version of [unsloth/Qwen2-7b](https://huggingface.co/unsloth/Qwen2-7b) on an dataset created by [Valerio Job](https://huggingface.co/valeriojob) based on real university lecture data. |
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- 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](https://www.fhnw.ch/en/degree-programmes/business/bsc-in-business-information-technology)). |
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- This repo includes the quantized models in the GGUF format. There is a separate repo called [valeriojob/flashcardsGPT-Qwen2-7B-v0.1](https://huggingface.co/valeriojob/flashcardsGPT-Qwen2-7B-v0.1) that includes the default format of the model as well as the LoRA adapters of the model. |
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- This model was quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp). |
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## Model description |
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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. |
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It uses the following Prompt Engineering template: |
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""" |
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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. |
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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. |
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Ensure the 'back' field contains no line breaks. |
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No additional text or explanation should be provided—only respond with the JSON object. |
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Here is the OCR-extracted text: |
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"""" |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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The dataset (train and test) used for fine-tuning this model can be found here: [datasets/valeriojob/FHNW-Flashcards-Data-v0.1](https://huggingface.co/datasets/valeriojob/FHNW-Flashcards-Data-v0.1) |
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## Licenses |
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- **License:** apache-2.0 |
