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Simplifying Medicine: Developing an Accessible Health Information Platform Niketkumardheeryan#880

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+Simplifying Medicine: Developing an Accessible Health Information Platform #880
+
+Problem description - Not everyone is familiar with all diseases and the medical terminology used by professionals. Therefore, an interface should be created to address this issue.
+
+Solution - To develop an LLM model trained on a dataset encompassing various medical terms and diseases, the fundamental concept is for users to inquire about specific diseases and receive relevant, accurate responses. I aim to leverage a pre-trained model, such as LLaMA 2, using Hugging Face
+
+##Dataset
+DS from huggingface : https://huggingface.co/datasets/gamino/wiki_medical_terms
+
+##Libraries Involved: 
+
+torch
+accelerate==0.21.0 
+peft==0.4.0 
+bitsandbytes==0.40.2 
+transformers==4.31.0 
+trl==0.4.7 
+gradio 
+
+## Steps Involved: 
+
+### Step 1: Installing and importing the libraries
+
+## Step 2: Loading the model
+pretrained model used - aboonaji/llama2finetune-v2
+
+## Step 3: Loading the tokenizer
+Leveraging AutoTokenizer.from_pretrained for the pretrained model used - aboonaji/llama2finetune-v2
+
+## Step 4: Setting the training arguments
+
+## Step 5: Creating the Supervised Fine-Tuning trainer
+Leveraging SFTTrainer
+
+## Step 6: Model Training
+
+## Step 7: Model Testing
+
+## Step 8: User Interface
+Leveraging Gradio ui