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+# 🧠 QuantLLM: Lightweight Library for Quantized LLM Fine-Tuning and Deployment
+
+## 📌 Overview
+
+**QuantLLM** is a Python library designed for developers, researchers, and teams who want to fine-tune and deploy large language models (LLMs) **efficiently** using **4-bit and 8-bit quantization** techniques. It provides a modular and flexible framework for:
+
+- **Loading and quantizing models** with advanced configurations
+- **LoRA / QLoRA-based fine-tuning** with customizable parameters
+- **Dataset management** with preprocessing and splitting
+- **Training and evaluation** with comprehensive metrics
+- **Model checkpointing** and versioning
+- **Hugging Face Hub integration** for model sharing
+
+The goal of QuantLLM is to **democratize LLM training**, especially in low-resource environments, while keeping the workflow intuitive, modular, and production-ready.
+
+## 🎯 Key Features
+
+| Feature                          | Description |
+|----------------------------------|-------------|
+| ✅ Quantized Model Loading       | Load any HuggingFace model in 4-bit or 8-bit precision with customizable quantization settings |
+| ✅ Advanced Dataset Management   | Load, preprocess, and split datasets with flexible configurations |
+| ✅ LoRA / QLoRA Fine-Tuning      | Memory-efficient fine-tuning with customizable LoRA parameters |
+| ✅ Comprehensive Training        | Advanced training loop with mixed precision, gradient accumulation, and early stopping |
+| ✅ Model Evaluation             | Flexible evaluation with custom metrics and batch processing |
+| ✅ Checkpoint Management        | Save, resume, and manage training checkpoints with versioning |
+| ✅ Hub Integration              | Push models and checkpoints to Hugging Face Hub with authentication |
+| ✅ Configuration Management     | YAML/JSON config support for reproducible experiments |
+| ✅ Logging and Monitoring       | Comprehensive logging and Weights & Biases integration |
+
+## 🚀 Getting Started
+
+### 🔧 Installation
+
+```bash
+pip install quantllm
+```
+
+### 📦 Basic Usage
+
+```python
+from quantllm import (
+    ModelLoader,
+    DatasetLoader,
+    DatasetPreprocessor,
+    DatasetSplitter,
+    FineTuningTrainer,
+    ModelEvaluator,
+    TrainingConfig,
+    ModelConfig,
+    DatasetConfig
+)
+
+# Initialize logger
+from quantllm.finetune import TrainingLogger
+logger = TrainingLogger()
+
+# 1. Dataset Configuration and Loading
+dataset_config = DatasetConfig(
+    dataset_name_or_path="imdb",
+    dataset_type="huggingface",
+    text_column="text",
+    label_column="label"
+)
+
+dataset_loader = DatasetLoader(logger)
+dataset = dataset_loader.load_hf_dataset(dataset_config.dataset_name_or_path)
+
+# 2. Model Configuration and Loading
+model_config = ModelConfig(
+    model_name_or_path="meta-llama/Llama-2-7b-hf",
+    load_in_4bit=True,
+    use_lora=True
+)
+
+model_loader = ModelLoader(
+    model_name=model_config.model_name_or_path,
+    quantization="4bit" if model_config.load_in_4bit else None,
+    use_lora=model_config.use_lora
+)
+model = model_loader.get_model()
+tokenizer = model_loader.get_tokenizer()
+
+# 3. Training Configuration
+training_config = TrainingConfig(
+    learning_rate=2e-4,
+    num_epochs=3,
+    batch_size=4
+)
+
+# 4. Initialize and Run Trainer
+trainer = FineTuningTrainer(
+    model=model,
+    training_config=training_config,
+    train_dataloader=train_dataloader,
+    eval_dataloader=val_dataloader,
+    logger=logger
+)
+trainer.train()
+```
+
+### ⚙️ Advanced Usage
+
+#### Configuration Files
+
+Create a config file (e.g., `config.yaml`):
+```yaml
+model:
+  model_name_or_path: "meta-llama/Llama-2-7b-hf"
+  load_in_4bit: true
+  use_lora: true
+  lora_config:
+    r: 16
+    lora_alpha: 32
+    target_modules: ["q_proj", "v_proj"]
+
+dataset:
+  dataset_name_or_path: "imdb"
+  text_column: "text"
+  label_column: "label"
+  max_length: 512
+
+training:
+  learning_rate: 2e-4
+  num_epochs: 3
+  batch_size: 4
+  gradient_accumulation_steps: 4
+```
+
+#### Hub Integration
+
+```python
+from quantllm.hub import HubManager
+
+hub_manager = HubManager(
+    model_id="your-username/llama-2-imdb",
+    token=os.getenv("HF_TOKEN")
+)
+
+if hub_manager.is_logged_in():
+    hub_manager.push_model(
+        model,
+        commit_message="Trained model with custom configuration"
+    )
+```
+
+#### Evaluation
+
+```python
+from quantllm.finetune import ModelEvaluator
+
+evaluator = ModelEvaluator(
+    model=model,
+    eval_dataloader=test_dataloader,
+    metrics=[
+        lambda preds, labels, _: (preds.argmax(dim=-1) == labels).float().mean().item()
+    ]
+)
+
+metrics = evaluator.evaluate()
+```
+
+## 📚 Documentation
+
+### Model Loading
+
+```python
+model_config = ModelConfig(
+    model_name_or_path="meta-llama/Llama-2-7b-hf",
+    load_in_4bit=True,
+    use_lora=True,
+    lora_config={
+        "r": 16,
+        "lora_alpha": 32,
+        "target_modules": ["q_proj", "v_proj"]
+    }
+)
+```
+
+### Dataset Management
+
+```python
+dataset_config = DatasetConfig(
+    dataset_name_or_path="imdb",
+    dataset_type="huggingface",
+    text_column="text",
+    label_column="label",
+    max_length=512,
+    train_size=0.8,
+    val_size=0.1,
+    test_size=0.1
+)
+```
+
+### Training Configuration
+
+```python
+training_config = TrainingConfig(
+    learning_rate=2e-4,
+    num_epochs=3,
+    batch_size=4,
+    gradient_accumulation_steps=4,
+    warmup_steps=100,
+    logging_steps=50,
+    eval_steps=200,
+    save_steps=500,
+    early_stopping_patience=3
+)
+```
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgments
+
+- [HuggingFace](https://huggingface.co/) for their amazing Transformers library
+- [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) for quantization
+- [PEFT](https://github.com/huggingface/peft) for parameter-efficient fine-tuning
+- [Weights & Biases](https://wandb.ai/) for experiment tracking