HumanTouch: AI Text Humanization with DoRA

Transform AI-generated text into natural, human-like writing using DoRA fine-tuned Qwen3-0.6B with 32k context support.

🎯 What is HumanTouch?

HumanTouch solves the problem of robotic, repetitive AI-generated content by converting it into natural, flowing human-like text while preserving the original meaning and context.

Key Features:

🧠 DoRA Fine-tuning - Advanced weight decomposition (rank 128) for superior quality
📝 32k Context - Handle long documents without truncation
🎯 High Quality - No quantization or Flash Attention compromises
🚀 Multi-GPU Support - Scale training across 2-8+ GPUs with DeepSpeed
⚡ Easy to Use - Simple Python scripts for training and inference
📈 Linear Scaling - Near-linear speedup with multiple GPUs

The Problem: AI text often sounds robotic, repetitive, and lacks natural human flow.

The Solution: HumanTouch learns from 500K human vs AI text pairs to add natural variations, improve flow, and maintain context coherence.

🚀 Multi-GPU Training Benefits

Why Multi-GPU?

Faster Training: 2-4x speedup with 4 GPUs, 4-8x speedup with 8 GPUs
Larger Models: Train bigger models that don't fit on single GPU
Better Quality: Larger effective batch sizes improve convergence
Cost Efficiency: Reduce training time and cloud costs significantly

Automatic Optimizations:

Learning Rate Scaling: Automatic sqrt scaling for stable multi-GPU training
Gradient Accumulation: Smart adjustment to maintain effective batch size
DeepSpeed Integration: ZeRO-2/3 optimization based on GPU count
Communication Efficient: Optimized data transfers between GPUs

📋 Requirements

Hardware

Single-GPU Setup:

GPU: A100 80GB, H100, or RTX 4090 24GB+
RAM: 32GB+ system memory
Storage: 100GB+ free space

Multi-GPU Setup (2-8 GPUs):

GPU: 2-8x A100 80GB, H100, RTX 4090 24GB+, or RTX A6000
RAM: 64GB+ system memory (scales with GPU count)
Storage: 200GB+ free space
Network: High-speed interconnect (NVLink, InfiniBand) recommended for 4+ GPUs
PSU: Adequate power supply for multiple high-end GPUs

Software

Python: 3.10 or higher
CUDA: 11.8+ or 12.1+ (for GPU support)
UV: Fast Python package manager

🚀 Manual Setup (Windows/Linux/Mac)

Step 1: Install UV Package Manager

Windows:

# PowerShell (as Administrator)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or via pip
pip install uv

Linux/Mac:

# Via curl
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or via pip  
pip install uv

Verify installation:

uv --version

Step 2: Clone and Setup Project

# Clone repository
git clone https://github.com/your-username/HumanTouch.git
cd HumanTouch

# Create virtual environment
uv venv

# Activate environment
# Windows:
.venv\Scripts\activate
# Linux/Mac:
source .venv/bin/activate

Step 3: Install Dependencies

Install PyTorch (choose your platform):

# CUDA 11.8
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# CUDA 12.1  
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# CPU only
uv pip install torch torchvision torchaudio

Install project dependencies:

uv pip install -r requirements.txt

Install DeepSpeed:

uv pip install deepspeed

Verify GPU setup:

python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
python -c "import torch; print(f'GPU count: {torch.cuda.device_count()}')"

Step 4: Create Directories

# Windows
mkdir data\raw data\processed data\evaluation models logs

# Linux/Mac  
mkdir -p data/{raw,processed,evaluation} models logs

Step 5: Download Dataset

Go to Kaggle: https://www.kaggle.com/datasets/shanegerami/ai-vs-human-text
Download the dataset (CSV file)
Place the CSV file in data/raw/ folder
Rename it to AI_Human_Text.csv (if needed)

Alternative - Kaggle API:

# Install Kaggle API
uv pip install kaggle

# Setup API key (see Kaggle docs)
kaggle datasets download -d shanegerami/ai-vs-human-text -p data/raw/

Step 6: Process Dataset

Basic processing:

python data_processing.py --input data/raw/AI_Human_Text.csv --output data/processed

With optional zip package creation:

python data_processing.py --input data/raw/AI_Human_Text.csv --output data/processed --create_zip

Expected output:

================================================================================
HumanTouch Data Processing
================================================================================
Input file: data/raw/AI_Human_Text.csv
Output directory: data/processed
Max sequence length: 32768
================================================================================
✓ Dataset loaded successfully!
  Shape: (487235, 2)
  Columns: ['text', 'generated']
  Generated distribution: {0.0: 305797, 1.0: 181438}

Filtering texts by length...
  Original samples: 487,231
  After length filtering: 487,182
  Average text length: 2263 characters
  Average estimated tokens: 566

Balancing dataset...
  AI texts: 181,419
  Human texts: 305,763
  Balanced dataset: 362,838 samples
  Final AI texts: 181,419
  Final human texts: 181,419

Creating training pairs...
  AI texts available: 181,419
  Human texts available: 181,419
  Creating 181,419 training pairs...
Creating pairs: 100%|████████████| 181419/181419 [00:01<00:00, 109313.90it/s]
✓ Created 181,419 training pairs

Sample processed data (showing 2 examples):
================================================================================
Sample 1:
AI Text: The artificial intelligence system processed...
Human Text: Our AI tool crunched through the data...
Full formatted text length: 5832 characters

Splitting and saving dataset to data/processed...
  Train samples: 145,135
  Validation samples: 18,142
  Test samples: 18,142
  Saving JSON files...
  Creating Hugging Face dataset...
  ✓ Saved Hugging Face dataset to data/processed/hf_dataset

✓ Dataset saved to data/processed
Files created:
  - train.json, validation.json, test.json
  - hf_dataset/ (Hugging Face format)
  - dataset_stats.json

🎉 DATA PROCESSING COMPLETE!
================================================================================
✓ Processed 181,419 training pairs
✓ Train: 145,135 samples
✓ Validation: 18,142 samples
✓ Test: 18,142 samples
✓ Files saved to data/processed/
✓ Max sequence length: 32768
================================================================================

Step 7: Train Model

🎯 Single-GPU Training (Interactive Mode Selection):

python train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch

🚀 Multi-GPU Training with DeepSpeed:

# 2 GPUs
deepspeed --num_gpus=2 train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode full

# 4 GPUs  
deepspeed --num_gpus=4 train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode full

# 8 GPUs (for high-end setups)
deepspeed --num_gpus=8 train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode full

# Interactive mode selection with multi-GPU
deepspeed --num_gpus=4 train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch

Training Mode Options: Both single-GPU and multi-GPU training offer 3 optimized configurations:

🎯 CHOOSE TRAINING MODE
================================================================================
🚀 Multi-GPU Mode: 4 GPUs Detected
================================================================================
1. Basic Training
   - Fast training for testing (2 epochs, small dataset) (Multi-GPU: 4 GPUs)
   - Model: Qwen/Qwen2.5-0.5B
   - DoRA Rank: 32
   - Max Length: 2048 tokens
   - Epochs: 2
   - Dataset Size: 5,000 train, 500 val
   - Learning Rate (scaled): 1.00e-04
   - Grad Accumulation (adjusted): 2

2. Full Training
   - Maximum quality training (6 epochs, large dataset) (Multi-GPU: 4 GPUs)
   - Model: Qwen/Qwen3-0.6B-Base
   - DoRA Rank: 128
   - Max Length: 8192 tokens
   - Epochs: 6
   - Dataset Size: 50,000 train, 5,000 val
   - Learning Rate (scaled): 1.60e-04
   - Grad Accumulation (adjusted): 4

3. Smaller Gpu Training
   - Balanced training for smaller GPUs (4 epochs, medium dataset) (Multi-GPU: 4 GPUs)
   - Model: Qwen/Qwen2.5-0.5B
   - DoRA Rank: 64
   - Max Length: 4096 tokens
   - Epochs: 4
   - Dataset Size: 20,000 train, 2,000 val
   - Learning Rate (scaled): 1.60e-04
   - Grad Accumulation (adjusted): 4

Enter your choice (1-3):

🚀 Direct Mode Specification:

Single-GPU:

# Basic training (fast, for testing)
python train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode basic

# Full training (maximum quality)
python train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode full

# Smaller GPU training (balanced)
python train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode smaller_gpu

Multi-GPU with DeepSpeed:

# Basic training (2 GPUs)
deepspeed --num_gpus=2 train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode basic

# Full training (4 GPUs)
deepspeed --num_gpus=4 train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode full

# Smaller GPU training (2 GPUs)
deepspeed --num_gpus=2 train.py --dataset_path data/processed/hf_dataset --output_dir models/humantouch --mode smaller_gpu

⚙️ Advanced Options with Overrides:

Single-GPU:

# Custom configuration with testing
python train.py \
    --dataset_path data/processed/hf_dataset \
    --output_dir models/humantouch \
    --mode full \
    --epochs 8 \
    --rank 256 \
    --test_model \
    --run_name "custom-experiment"

# Training with specific model
python train.py \
    --dataset_path data/processed/hf_dataset \
    --output_dir models/humantouch \
    --mode smaller_gpu \
    --model_name "Qwen/Qwen2.5-1.5B" \
    --max_length 8192

Multi-GPU:

# Custom multi-GPU configuration
deepspeed --num_gpus=4 train.py \
    --dataset_path data/processed/hf_dataset \
    --output_dir models/humantouch \
    --mode full \
    --epochs 8 \
    --rank 256 \
    --test_model \
    --run_name "multi-gpu-experiment"

# High-end 8-GPU setup
deepspeed --num_gpus=8 train.py \
    --dataset_path data/processed/hf_dataset \
    --output_dir models/humantouch \
    --mode full \
    --model_name "Qwen/Qwen2.5-1.5B" \
    --max_length 8192

📊 Training Features:

Multi-GPU Support: Automatic multi-GPU detection and DeepSpeed integration (2-8+ GPUs)
GPU Detection: Automatic GPU memory and capability detection
Learning Rate Scaling: Automatic sqrt scaling for multi-GPU setups
Gradient Accumulation: Smart adjustment to maintain effective batch size
Progress Monitoring: Real-time training progress with WandB integration
Memory Optimization: Automatic DeepSpeed configuration based on mode and GPU count
Communication Optimization: Efficient data transfers for multi-GPU training
Error Handling: Comprehensive error recovery and debugging
Model Testing: Built-in model testing after training completion
Configuration Saving: All training settings saved for reproducibility

⏱️ Expected Training Times:

Single-GPU:

Basic Mode: 30-60 minutes (testing/prototyping)
Smaller GPU Mode: 8-15 hours (RTX 4090, etc.)
Full Mode: 15-25 hours (A100/H100)

Multi-GPU (with linear scaling):

Basic Mode: 15-30 minutes (2-4 GPUs)
Smaller GPU Mode: 2-8 hours (2-4 GPUs)
Full Mode: 4-12 hours (2-4 GPUs), 2-6 hours (4-8 GPUs)

💡 Training Tips:

Single-GPU:

# Monitor GPU usage
nvidia-smi

# Training with custom WandB project
python train.py --dataset_path data/processed --output_dir models/humantouch --mode full --run_name "production-v1"

# Disable WandB logging
python train.py --dataset_path data/processed --output_dir models/humantouch --mode basic --no_wandb

# Test model after training
python train.py --dataset_path data/processed --output_dir models/humantouch --mode basic --test_model

Multi-GPU:

# Monitor all GPUs
watch -n 1 nvidia-smi

# Multi-GPU training with WandB
deepspeed --num_gpus=4 train.py --dataset_path data/processed --output_dir models/humantouch --mode full --run_name "multi-gpu-v1"

# Check DeepSpeed installation
python -c "import deepspeed; print(f'DeepSpeed version: {deepspeed.__version__}')"

# Multi-GPU test after training
deepspeed --num_gpus=2 train.py --dataset_path data/processed --output_dir models/humantouch --mode basic --test_model

Step 8: Evaluate Model

Built-in model testing (during training):

# Test model automatically after training
python train.py --dataset_path data/processed --output_dir models/humantouch --mode basic --test_model

Comprehensive evaluation:

# Full evaluation suite
python evaluate.py \
    --model_path models/humantouch \
    --test_dataset data/processed/hf_dataset \
    --max_samples 500 \
    --output_dir evaluation_results

# Quick single text test
python evaluate.py \
    --model_path models/humantouch \
    --quick_test \
    --text "The artificial intelligence system processed the data efficiently and generated comprehensive analytical reports."

Expected test output:

============================================================
MODEL TEST RESULTS
============================================================
Input: The artificial intelligence system processed the data efficiently...
Output: Our AI tool crunched through the data pretty efficiently and came up with some detailed analysis reports.
============================================================

Step 9: Use for Inference

Interactive mode:

python inference.py --model_path models/humantouch --interactive

Single text:

python inference.py \
    --model_path models/humantouch \
    --text "Artificial intelligence algorithms demonstrate significant capabilities in processing and analyzing large datasets."

Batch processing:

# Create input file
echo ["Text 1 to humanize", "Text 2 to humanize"] > input_texts.json

# Process batch
python inference.py \
    --model_path models/humantouch \
    --input_file input_texts.json \
    --output_file humanized_results.json

📁 Project Structure

HumanTouch/
├── data_processing.py      # Enhanced dataset preparation with progress tracking
├── train.py               # Interactive DoRA training with 3 modes
├── evaluate.py            # Model evaluation script
├── inference.py           # Text humanization script
├── requirements.txt       # Python dependencies
├── configs/
│   └── dora_config.yaml   # DoRA configuration settings
├── colab_ready/           # Google Colab versions
│   ├── data_processing_colab.py
│   └── train_colab.py
├── data/
│   ├── raw/              # Original Kaggle dataset
│   ├── processed/        # Processed training data
│   └── evaluation/       # Evaluation results
├── models/               # Trained model checkpoints
└── logs/                 # Training logs

⚙️ Configuration Options

🎯 Training Modes (Interactive)

The new interactive training system provides three optimized configurations:

# Interactive mode selection (recommended)
python train.py --dataset_path data/processed --output_dir models/humantouch

# Direct mode selection
python train.py --dataset_path data/processed --output_dir models/humantouch --mode [basic|full|smaller_gpu]

Mode Comparison:

Mode	Model	Rank	Length	Epochs	Time	Quality
Basic	Qwen2.5-0.5B	32	2048	2	30-60min	Testing
Smaller GPU	Qwen2.5-0.5B	64	4096	4	8-15hrs	Good
Full	Qwen3-0.6B	128	8192	6	15-25hrs	Best

🔧 Advanced Configuration

Custom parameter overrides:

# Override specific parameters
python train.py \
    --dataset_path data/processed \
    --output_dir models/humantouch \
    --mode full \
    --rank 256 \
    --epochs 8 \
    --max_length 16384 \
    --model_name "Qwen/Qwen2.5-1.5B"

# Memory-optimized training
python train.py \
    --dataset_path data/processed \
    --output_dir models/humantouch \
    --mode smaller_gpu \
    --max_length 2048 \
    --rank 32

Inference Parameters

# High quality (slower)
python inference.py --model_path models/humantouch --max_tokens 2048 --temperature 0.7

# Faster generation
python inference.py --model_path models/humantouch --max_tokens 1024 --temperature 0.8

# More creative
python inference.py --model_path models/humantouch --temperature 0.9

# More conservative  
python inference.py --model_path models/humantouch --temperature 0.5

🛠️ Troubleshooting

GPU Memory Issues

Single-GPU - CUDA out of memory during training:

# Solution 1: Use Basic mode for testing
python train.py --dataset_path data/processed --output_dir models/humantouch --mode basic

# Solution 2: Reduce parameters manually
python train.py --dataset_path data/processed --output_dir models/humantouch --mode smaller_gpu --max_length 2048 --rank 32

# Solution 3: Check GPU memory
python -c "import torch; print(f'GPU Memory: {torch.cuda.get_device_properties(0).total_memory/1e9:.1f} GB')"

Multi-GPU - Memory or communication issues:

# Solution 1: Check all GPUs
nvidia-smi

# Solution 2: Test with fewer GPUs
deepspeed --num_gpus=2 train.py --dataset_path data/processed --output_dir models/humantouch --mode basic

# Solution 3: Check GPU memory across all devices
python -c "import torch; [print(f'GPU {i}: {torch.cuda.get_device_properties(i).total_memory/1e9:.1f} GB') for i in range(torch.cuda.device_count())]"

# Solution 4: Use smaller model for multi-GPU
deepspeed --num_gpus=4 train.py --dataset_path data/processed --output_dir models/humantouch --mode smaller_gpu

Training Issues

Single-GPU - Training fails or is very slow:

# Check system status
nvidia-smi
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else "None"}')"

# Use Basic mode for quick testing
python train.py --dataset_path data/processed --output_dir models/test --mode basic --test_model

# Check DeepSpeed installation
python -c "import deepspeed; print('DeepSpeed OK')"

Multi-GPU - Training fails or communication issues:

# Check DeepSpeed installation with multi-GPU support
python -c "import deepspeed; print(f'DeepSpeed version: {deepspeed.__version__}')"

# Test basic multi-GPU functionality
deepspeed --num_gpus=2 train.py --dataset_path data/processed --output_dir models/test --mode basic

# Check GPU topology and communication
nvidia-smi topo -m

# Verify all GPUs are visible
python -c "import torch; print(f'Total GPUs: {torch.cuda.device_count()}')"

# Run with debugging enabled
NCCL_DEBUG=INFO deepspeed --num_gpus=2 train.py --dataset_path data/processed --output_dir models/test --mode basic

Data Processing Issues

Problem: Dataset processing fails

# Check input file
head -5 data/raw/AI_Human_Text.csv

# Process with debugging
python data_processing.py --input data/raw/AI_Human_Text.csv --output data/processed --create_zip

# Verify output
ls -la data/processed/
head -2 data/processed/train.json

Quality Optimization

Problem: Generated text quality needs improvement

# Use Full mode for best quality
python train.py --dataset_path data/processed --output_dir models/humantouch --mode full

# Increase training with custom parameters
python train.py --dataset_path data/processed --output_dir models/humantouch --mode full --epochs 8 --rank 256

# Test different models
python train.py --dataset_path data/processed --output_dir models/humantouch --mode full --model_name "Qwen/Qwen2.5-1.5B"

Installation Issues

Problem: Package installation fails

# Update UV
uv self update

# Clear cache
uv cache clean

# Try pip instead
pip install -r requirements.txt

# Check Python version
python --version  # Should be 3.10+

Model Loading Issues

Problem: Cannot load trained model

# Verify model files exist
# Windows: dir models\humantouch
# Linux/Mac: ls -la models/humantouch/

# Check PEFT installation
python -c "from peft import PeftModel; print('PEFT OK')"

# Test base model loading
python -c "from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('Qwen/Qwen3-0.6B-Base')"

📊 Expected Results

Training Metrics

Final Loss: < 1.5
Eval Loss: < 2.0
Training Time: 48-120 hours
Model Size: ~2-5GB

Quality Metrics

ROUGE-L F1: 0.55-0.65
BLEU Score: 0.35-0.45
Human Rating: 8/10+
Perplexity: 2.0-3.0

Example Results

Input: "The artificial intelligence system processed the data efficiently and generated comprehensive analytical reports."

Output: "Our AI tool crunched through the data pretty efficiently and came up with some detailed analysis reports."

🎯 Use Cases

Content Creation: Humanize AI-generated articles, blogs, marketing copy
Academic Writing: Improve AI-assisted research papers and reports
Documentation: Make technical documentation more readable
Creative Writing: Add human touch to AI-generated stories
Business: Natural-sounding AI communication and reports

🔗 Advanced Features

📁 Google Colab Support

For Google Colab users, we provide specialized scripts:

# Use the Colab-ready scripts
colab_ready/
├── data_processing_colab.py  # Auto-installs packages, Drive integration
└── train_colab.py           # Memory-optimized for Colab

Colab Features:

📦 Auto-installation of all dependencies
💾 Google Drive integration for dataset storage
🧠 Memory optimization for free/pro Colab
📊 Progress tracking with download packages
⚙️ Same 3 training modes as local version

📈 Dataset Requirements

Input CSV Format:

text: The text content
generated: 1 for AI text, 0 for human text
prompt: (optional) prompt used to generate text

Output Structure:

data/processed/
├── train.json              # Training data (80%)
├── validation.json         # Validation data (10%)
├── test.json               # Test data (10%)
├── dataset_stats.json      # Dataset statistics
├── hf_dataset/             # HuggingFace format
└── humantouch_processed_data.zip  # Complete package

📉 WandB Integration

# Setup WandB (optional)
uv pip install wandb
wandb login

# Training automatically logs to WandB
python train.py --dataset_path data/processed --output_dir models/humantouch --mode full

# Disable WandB if needed
python train.py --dataset_path data/processed --output_dir models/humantouch --mode full --no_wandb

# Custom run names
python train.py --dataset_path data/processed --output_dir models/humantouch --mode full --run_name "production-v1"

🤝 Contributing

Fork the repository
Create feature branch: git checkout -b feature/amazing-feature
Make changes and test them
Format code: black . && isort . (optional)
Commit: git commit -m 'Add amazing feature'
Push: git push origin feature/amazing-feature
Open pull request

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Qwen Team - Qwen3 base model
NVIDIA Research - DoRA methodology
Hugging Face - PEFT and Transformers
Microsoft - DeepSpeed optimization
Kaggle - AI vs Human text dataset

📞 Support

Issues: GitHub Issues for bug reports
Questions: GitHub Discussions for help
Documentation: This README + code comments

🚀 Quick Start Summary

# 1. Setup environment
git clone https://github.com/your-username/HumanTouch.git
cd HumanTouch
uv venv && source .venv/bin/activate  # or .venv\Scripts\activate on Windows
uv pip install -r requirements.txt

# 2. Process your dataset
python data_processing.py --input data/raw/AI_Human_Text.csv --output data/processed

# 3. Train with interactive mode selection
python train.py --dataset_path data/processed --output_dir models/humantouch

# 4. Test your model
python train.py --dataset_path data/processed --output_dir models/humantouch --mode basic --test_model

🎉 Ready to start? The enhanced interactive system will guide you through the entire process!

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
colab_ready		colab_ready
configs		configs
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
data_processing.py		data_processing.py
evaluate.py		evaluate.py
inference.py		inference.py
requirements.txt		requirements.txt
train.py		train.py

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HumanTouch: AI Text Humanization with DoRA

🎯 What is HumanTouch?

🚀 Multi-GPU Training Benefits

📋 Requirements

Hardware

Software

🚀 Manual Setup (Windows/Linux/Mac)

Step 1: Install UV Package Manager

Step 2: Clone and Setup Project

Step 3: Install Dependencies

Step 4: Create Directories

Step 5: Download Dataset

Step 6: Process Dataset

Step 7: Train Model

Step 8: Evaluate Model

Step 9: Use for Inference

📁 Project Structure

⚙️ Configuration Options

🎯 Training Modes (Interactive)

🔧 Advanced Configuration

Inference Parameters

🛠️ Troubleshooting

GPU Memory Issues

Training Issues

Data Processing Issues

Quality Optimization

Installation Issues

Model Loading Issues

📊 Expected Results

Training Metrics

Quality Metrics

Example Results

🎯 Use Cases

🔗 Advanced Features

📁 Google Colab Support

📈 Dataset Requirements

📉 WandB Integration

🤝 Contributing

📄 License

🙏 Acknowledgments

📞 Support

🚀 Quick Start Summary

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