Update README.md

README.md

@@ -5,6 +5,9 @@ license: mit
 language:
 - en
 pipeline_tag: text-generation
+tags:
+- educational
+- storytelling
 ---
 
 # Model Card for Educational Storytelling in Computer Science
@@ -15,13 +18,13 @@ This model, developed using Hugging Face’s transformer library, is designed fo
 
 ### Model Description
 
-This model is an innovative tool for teaching fundamental computer science concepts via educational storytelling. It generates interactive narratives tailored to specific CS topics requested by the user, such as loops, incorporating assessments to enhance learning and engagement.
+This model is an innovative tool for teaching fundamental computer science concepts via educational storytelling. It generates interactive stories tailored to specific CS topics requested by the user, such as algorithms, programming basics & more, incorporating assessments to enhance learning and engagement.
 
-- **Developed by:** Rana M Khamoud
+- **Developed by:** Ranam Hamoud & George Kanaan
 - **Model type:** PEFT adapter model using LoRA from Meta's Llama2 7B
 - **Language(s) (NLP):** English
-- **License:** Specify License
-- **Finetuned from model [optional]:** Finetuned from Meta's Llama-2 7B model
+- **License:** MIT License
+- **Finetuned from model :** Finetuned from Meta's Llama-2 7B model
 
 ### Model Sources
 
@@ -35,7 +38,7 @@ This model is an innovative tool for teaching fundamental computer science conce
 
 The model is designed to be used directly via an interactive interface where users can ask for stories about specific computer science topics. It's suitable for educational purposes, particularly in learning environments or as a supplementary learning tool.
 
-### Downstream Use [optional]
+### Downstream Use
 
 While primarily designed for educational storytelling, the model could potentially be adapted for other educational applications or interactive learning tools that require narrative generation.
 
@@ -61,12 +64,6 @@ Here's a general framework for initializing and running the model, detailed in t
 
 The model was trained on a custom dataset generated specifically for this project, aimed at creating educational content related to computer science topics. The data generation scripts and datasets are available at the linked GitHub repository.
 
-### Training Procedure
-
-#### Preprocessing
-
-Specific preprocessing details were not provided but would typically include data cleaning and formatting to fit the model's input requirements.
-
 #### Training Hyperparameters
 
 The model was trained on an NVIDIA A100 machine using quantization techniques to optimize performance. Training involved configurations like LoRA adaptation and fine-tuning of Meta's Llama2 7B model under specified training arguments.
@@ -75,18 +72,17 @@ The model was trained on an NVIDIA A100 machine using quantization techniques to
 
 ### Testing Data, Factors & Metrics
 
-Further details on testing data and evaluation metrics are needed to provide insight into the model’s performance and accuracy.
+Further details on testing data and evaluation metrics will be provided.
 
 ### Results
 
-Results of the training and subsequent evaluations need to be provided to understand the effectiveness of the model in educational storytelling.
+Results of the training and subsequent evaluations will be provided to understand the effectiveness of the model in educational storytelling.
 
 ## Environmental Impact
 
 - **Hardware Type:** NVIDIA A100
-- **Hours used:** 5 hours
+- **Hours used:** 8 hours
 - **Cloud Provider:** RunPod
-- **Compute Region:** Not specified (please provide if available)
 - **Carbon Emitted:** Estimates not provided
 
 [More Information Needed]