Update README.md

# 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

README.md

@@ -1,5 +1,10 @@
 ---
 library_name: peft
+datasets:
+- xsum
+language:
+- en
+pipeline_tag: summarization
 ---
 ## Training procedure
 
@@ -19,3 +24,5 @@ The following `bitsandbytes` quantization config was used during training:
 
 
 - PEFT 0.5.0
+
+