Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/qwen2-7b-bnb-4bit
+base_model: unsloth/Qwen2-7B
 language:
 - en
 license: apache-2.0
@@ -7,16 +7,38 @@ tags:
 - text-generation-inference
 - transformers
 - unsloth
-- qwen2
+- llama
 - gguf
 ---
 
-# Uploaded  model
+# flashcardsGPT-Qwen2-7B-v0.1-GGUF
 
-- **Developed by:** valeriojob
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2-7b-bnb-4bit
+- 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.
+- 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)).
+- 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.
+- This model was quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp).
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Model description
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+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](https://huggingface.co/datasets/valeriojob/FHNW-Flashcards-Data-v0.1)
+
+## Licenses
+- **License:** apache-2.0