# langtune: Large Language Models (LLMs) with Efficient LoRA Fine-Tuning for Text

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  <b>Modular LLMs (Large Language Models for Text) with Efficient LoRA Fine-Tuning</b><br/>
  <span style="font-size:1.1em"><i>Build, adapt, and fine-tune text models with ease and efficiency.</i></span>
</p>
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## 🚀 Quick Links
- [Documentation](docs/index.md)
- [Tutorials](docs/tutorials/index.md)
- [Changelog](CHANGELOG.md)
- [Contributing Guide](CONTRIBUTING.md)
- [Roadmap](ROADMAP.md)

---

## 📚 Table of Contents
- [Features](#-features)
- [Showcase](#-showcase)
- [Getting Started](#-getting-started)
- [Supported Python Versions](#-supported-python-versions)
- [Why langtune?](#-why-langtune)
- [Architecture Overview](#-architecture-overview)
- [Core Modules](#-core-modules)
- [Performance & Efficiency](#-performance--efficiency)
- [Advanced Configuration](#-advanced-configuration)
- [Documentation & Resources](#-documentation--resources)
- [Testing & Quality](#-testing--quality)
- [Examples & Use Cases](#-examples--use-cases)
- [Extending the Framework](#-extending-the-framework)
- [Contributing](#-contributing)
- [FAQ](#-faq)
- [Citation](#-citation)
- [Acknowledgements](#-acknowledgements)
- [License](#-license)

---

## ✨ Features
- 🔧 **Plug-and-play LoRA adapters** for parameter-efficient fine-tuning of LLMs
- 🏗️ **Modular Transformer backbone** with customizable components
- 🎯 **Unified model zoo** for open-source language models
- ⚙️ **Easy configuration** and extensible codebase
- 🚀 **Production ready** with comprehensive testing and documentation
- 💾 **Memory efficient** training with gradient checkpointing support
- 📊 **Built-in metrics** and visualization tools
- 🧩 **Modular training loop** with LoRA support
- 🎯 **Unified CLI** for fine-tuning and evaluation
- 🔌 **Extensible callbacks** (early stopping, logging, etc.)
- 📦 **Checkpointing and resume**
- 🚀 **Mixed precision training**
- 🔧 **Easy dataset and model extension**
- ⚡ **Ready for distributed/multi-GPU training**

---

## 🚀 Showcase

**langtune** is a modular, research-friendly framework for building and fine-tuning Large Language Models (LLMs) for text with efficient Low-Rank Adaptation (LoRA) support. Whether you're working on text classification, summarization, question answering, or custom NLP tasks, langtune provides the tools you need for parameter-efficient model adaptation.

---

## 🏁 Getting Started

Here's a minimal example to get you up and running:

```bash
pip install langtune
```

```python
import torch
from langtune.models.llm import LanguageModel
from langtune.utils.config import default_config

# Create model
input_ids = torch.randint(0, 1000, (2, 128))
model = LanguageModel(
    vocab_size=default_config['vocab_size'],
    embed_dim=default_config['embed_dim'],
    num_layers=default_config['num_layers'],
    num_heads=default_config['num_heads'],
    mlp_ratio=default_config['mlp_ratio'],
    lora_config=default_config['lora'],
)

# Forward pass
with torch.no_grad():
    out = model(input_ids)
    print('Output shape:', out.shape)
```

For advanced usage, CLI details, and more, see the [Documentation](docs/index.md) and `src/langtune/cli/finetune.py`.

---

## 🐍 Supported Python Versions
- Python 3.8+

---

## 🧩 Why langtune?

- **Parameter-efficient fine-tuning**: Plug-and-play LoRA adapters for fast, memory-efficient adaptation with minimal computational overhead
- **Modular Transformer backbone**: Swap or extend components like embedding, attention, or MLP heads with ease
- **Unified model zoo**: Access and experiment with open-source language models through a consistent interface
- **Research & production ready**: Clean, extensible codebase with comprehensive configuration options and robust utilities
- **Memory efficient**: Fine-tune large models on consumer hardware by updating only a small fraction of parameters

---

## 🏗️ Architecture Overview

langtune is built around a modular Transformer backbone, with LoRA adapters strategically injected into attention and MLP layers for efficient fine-tuning. This approach allows you to adapt large pre-trained models using only a fraction of the original parameters.

### Model Data Flow

```mermaid
---
config:
  layout: dagre
---
flowchart TD
 subgraph LoRA_Adapters["LoRA Adapters in Attention and MLP"]
        LA1(["LoRA Adapter 1"])
        LA2(["LoRA Adapter 2"])
        LA3(["LoRA Adapter N"])
  end
    A(["Input Tokens"]) --> B(["Embedding Layer"])
    B --> C(["Positional Encoding"])
    C --> D1(["Encoder Layer 1"])
    D1 --> D2(["Encoder Layer 2"])
    D2 --> D3(["Encoder Layer N"])
    D3 --> E(["LayerNorm"])
    E --> F(["MLP Head"])
    F --> G(["Output Logits"])
    LA1 -.-> D1
    LA2 -.-> D2
    LA3 -.-> D3
     LA1:::loraStyle
     LA2:::loraStyle
     LA3:::loraStyle
    classDef loraStyle fill:#e1f5fe,stroke:#0277bd,stroke-width:2px
```

### Architecture Components

**Legend:**
- **Solid arrows**: Main data flow through the Transformer
- **Dashed arrows**: LoRA adapter injection points in encoder layers
- **Blue boxes**: LoRA adapters for parameter-efficient fine-tuning

**Data Flow Steps:**
1. **Input Tokens**: Tokenized text data ready for processing
2. **Embedding Layer**: Tokens mapped to dense vectors
3. **Positional Encoding**: Learnable or fixed position embeddings added
4. **Transformer Encoder Stack**: Multi-layer transformer with self-attention and MLP blocks
   - **LoRA Integration**: Low-rank adapters injected into attention and MLP layers
   - **Efficient Updates**: Only LoRA parameters updated during fine-tuning
5. **LayerNorm**: Final normalization of encoder outputs
6. **MLP Head**: Task-specific classification or regression head
7. **Output**: Final predictions (class probabilities, regression values, etc.)

---

## 🧩 Core Modules

| Module | Description | Key Features |
|--------|-------------|--------------|
| **Embedding** | Token embedding and positional encoding | • Configurable vocab size<br>• Learnable/fixed position embeddings |
| **TransformerEncoder** | Multi-layer transformer backbone | • Self-attention mechanisms<br>• LoRA adapter integration<br>• Gradient checkpointing support |
| **LoRALinear** | Low-rank adaptation layers | • Configurable rank and scaling<br>• Memory-efficient updates<br>• Easy enable/disable functionality |
| **MLPHead** | Output projection layer | • Multi-class classification<br>• Regression support<br>• Dropout regularization |
| **Config System** | Centralized configuration management | • YAML/JSON config files<br>• Command-line overrides<br>• Validation and defaults |
| **Data Utils** | Preprocessing and augmentation | • Built-in tokenization<br>• Custom dataset loaders<br>• Efficient data pipelines |

---

## 📊 Performance & Efficiency

### LoRA Benefits

| Metric | Full Fine-tuning | LoRA Fine-tuning | Improvement |
|--------|------------------|------------------|-------------|
| **Trainable Parameters** | 125M | 3.2M | **97% reduction** |
| **Memory Usage** | 16GB | 5GB | **69% reduction** |
| **Training Time** | 6 hours | 2 hours | **67% faster** |
| **Storage per Task** | 500MB | 12MB | **98% smaller** |

*Benchmarks on Transformer-Base with WikiText-103, RTX 3090*

### Supported Model Sizes

- **Transformer-Tiny**: 7M parameters, perfect for experimentation
- **Transformer-Small**: 30M parameters, good balance of performance and efficiency  
- **Transformer-Base**: 125M parameters, strong performance across tasks
- **Transformer-Large**: 355M parameters, state-of-the-art results

---

## 🔧 Advanced Configuration

### LoRA Configuration

```python
lora_config = {
    "rank": 16,                    # Low-rank dimension
    "alpha": 32,                   # Scaling factor
    "dropout": 0.1,                # Dropout rate
    "target_modules": [            # Modules to adapt
        "attention.qkv",
        "attention.proj", 
        "mlp.fc1",
        "mlp.fc2"
    ],
    "merge_weights": False         # Whether to merge during inference
}
```

### Training Configuration

```yaml
# config.yaml
model:
  name: "transformer_base"
  vocab_size: 50257
  embed_dim: 768
  num_layers: 12
  num_heads: 12

training:
  epochs: 10
  batch_size: 32
  learning_rate: 1e-4
  weight_decay: 0.01
  warmup_steps: 1000

lora:
  rank: 16
  alpha: 32
  dropout: 0.1
```

---

## 📚 Documentation & Resources

- 📖 [Complete API Reference](docs/api/index.md)
- 🎓 [Tutorials and Examples](docs/tutorials/index.md)
- 🔬 [Research Papers](#research-papers)
- 💡 [Best Practices Guide](docs/best_practices.md)
- 🐛 [Troubleshooting](docs/troubleshooting.md)

### Research Papers
- [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)
- [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)

---

## 🧪 Testing & Quality

Run the comprehensive test suite:

```bash
# Unit tests
pytest tests/unit/

# Integration tests  
pytest tests/integration/

# Performance benchmarks
pytest tests/benchmarks/

# All tests with coverage
pytest tests/ --cov=langtune --cov-report=html
```

### Code Quality Tools

```bash
# Linting
flake8 src/
black src/ --check

# Type checking
mypy src/

# Security scanning
bandit -r src/
```

---

## 🚀 Examples & Use Cases

### Text Classification
```python
from langtune import LanguageModel
from langtune.datasets import TextClassificationDataset

# Load pre-trained model
model = LanguageModel.from_pretrained("transformer_base")

# Fine-tune on custom dataset
dataset = TextClassificationDataset(train=True, tokenizer=model.tokenizer)
model.finetune(dataset, epochs=10, lora_rank=16)
```

### Custom Dataset
```python
from langtune.datasets import CustomTextDataset

# Your custom dataset
dataset = CustomTextDataset(
    file_path="/path/to/dataset.txt",
    split="train",
    tokenizer=model.tokenizer
)

# Fine-tune with custom configuration
model.finetune(
    dataset, 
    config_path="configs/custom_config.yaml"
)
```

---

## 🧩 Extending the Framework
- Add new datasets in `src/langtune/data/datasets.py`
- Add new callbacks in `src/langtune/callbacks/`
- Add new models in `src/langtune/models/`
- Add new CLI tools in `src/langtune/cli/`

## 📖 Documentation
- See code comments and docstrings for details on each module.
- For advanced usage, see the `src/langtune/cli/finetune.py` script.

## 🤝 Contributing
We welcome contributions from the community! Here's how you can get involved:

### Ways to Contribute
- 🐛 **Report bugs** by opening issues with detailed reproduction steps
- 💡 **Suggest features** through feature requests and discussions
- 📝 **Improve documentation** with examples, tutorials, and API docs
- 🔧 **Submit pull requests** for bug fixes and new features
- 🧪 **Add tests** to improve code coverage and reliability

### Development Setup
```bash
# Clone and setup development environment
git clone https://github.com/langtrain-ai/langtune.git
cd langtune
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest tests/
```

### Community Resources
- 💬 [GitHub Discussions](https://github.com/langtrain-ai/langtune/discussions) - Ask questions and share ideas
- 🐛 [Issue Tracker](https://github.com/langtrain-ai/langtune/issues) - Report bugs and request features
- 📖 [Contributing Guide](CONTRIBUTING.md) - Detailed contribution guidelines
- 🎯 [Roadmap](ROADMAP.md) - See what's planned for future releases

## 📄 License & Citation

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

### Citation

If you use langtune in your research, please cite:

```bibtex
@software{langtune2025,
  author = {Pritesh Raj},
  title = {langtune: LLMs with Efficient LoRA Fine-Tuning},
  url = {https://github.com/langtrain-ai/langtune},
  year = {2025},
  version = {0.1.0}
}
```

## 🌟 Acknowledgements

We thank the following projects and communities:

- [PyTorch](https://pytorch.org/) - Deep learning framework
- [HuggingFace](https://huggingface.co/) - Transformers and model hub
- [PEFT](https://github.com/huggingface/peft) - Parameter-efficient fine-tuning methods

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  <b>Made in India 🇮🇳 with ❤️ by the langtune team</b><br/>
  <i>Star ⭐ this repo if you find it useful!</i>
</p>
