Update readme

README.md

@@ -25,4 +25,51 @@ The following `bitsandbytes` quantization config was used during training:
 
 - PEFT 0.5.0
 
+# Project Title
+
+Short description of your project or the model you've fine-tuned.
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Training Procedure](#training-procedure)
+- [Quantization Configuration](#quantization-configuration)
+- [Framework Versions](#framework-versions)
+- [Usage](#usage)
+- [Evaluation](#evaluation)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Overview
+
+Provide a brief introduction to your project. Explain what your fine-tuned model does and its potential applications. Mention any notable achievements or improvements over the base model.
+
+## Training Procedure
+
+Describe the training process for your fine-tuned model. Include details such as:
+- Dataset used (XSum).
+- Amount of data used (3% of the dataset).
+- Number of training epochs (1 epoch).
+- Any specific data preprocessing or augmentation.
+
+## Quantization Configuration
+
+Explain the quantization configuration used during training. Include details such as:
+- Quantization method (bitsandbytes).
+- Whether you loaded data in 8-bit or 4-bit.
+- Threshold and skip modules for int8 quantization.
+- Use of FP32 CPU offload and FP16 weight.
+- Configuration for 4-bit quantization (fp4, double quant, compute dtype).
+
+## Framework Versions
+
+List the versions of the frameworks or libraries you used for this project. Include specific versions, e.g., PEFT 0.5.0.
+
+## Usage
+
+Provide instructions on how to use your fine-tuned model. Include code snippets or examples on how to generate summaries using the model. Mention any dependencies that need to be installed.
+
+```bash
+# Example usage command
+python generate_summary.py --model your-model-name --input input.txt --output output.txt