Update README.md

README.md

@@ -9,4 +9,37 @@ datasets:
 - oldflag/symptom_dx_test
 pipeline_tag: question-answering
 ---
-TEST
+# Fine-Tuning Llama3-8b-bnb-4bit Model for Medical Symptom Diagnosis
+
+This project demonstrates how to fine-tune the Llama3-8b-bnb-4bit model using a Question and Answer dataset focused on medical symptoms and their diagnoses.   
+The project is implemented using Google Colab and utilizes the `unsloth` library for efficient model handling.
+
+## Overview
+
+The goal of this project is to fine-tune the Llama3-8b-bnb-4bit model to generate accurate medical diagnoses based on input symptoms.   
+This is achieved by using a dataset of medical Q&A pairs and adapting the model to understand and respond to medical queries effectively.
+
+## Setup and Installation
+
+1. **Clone the repository and navigate to the project directory:**
+
+   ```bash
+   git clone https://github.com/oldfalg/FineTuning_Llama_3_8b_Symptom_Dx.git
+   cd FineTuning_Llama_3_8b_Symptom_Dx
+
+
+## Key Components
+
+•	Model Loading:   
+Utilizes the FastLanguageModel from the unsloth library to load the pre-trained Llama3-8b-bnb-4bit model with 4-bit quantization for efficient memory usage.   
+•	Dataset Preparation:   
+Uses the datasets library to load and process a Q&A dataset for fine-tuning.   
+•	Fine-Tuning:   
+Fine-tunes the model in Colab to generate accurate diagnoses based on input symptoms.   
+•	Model Uploading:   
+Supports saving the fine-tuned model in different formats (float16, int4, and LoRA adapters) and uploading it to Hugging Face.   
+
+Inference
+
+After fine-tuning, the model can be used to generate diagnoses based on new symptom inputs.   
+The project supports enabling native faster inference and using the fine-tuned model for generation tasks.