langtune 0.1.0

A package for finetuning text models.

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

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Modular LLMs (Large Language Models for Text) with Efficient LoRA Fine-Tuning
Build, adapt, and fine-tune text models with ease and efficiency.

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🚀 Quick Links

• Documentation
• Tutorials
• Changelog
• Contributing Guide
• Roadmap

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📚 Table of Contents

• Features
• Showcase
• Getting Started
• Supported Python Versions
• Why langtune?
• Architecture Overview
• Core Modules
• Performance & Efficiency
• Advanced Configuration
• Documentation & Resources
• Testing & Quality
• Examples & Use Cases
• Extending the Framework
• Contributing
• FAQ
• Citation
• Acknowledgements
• License

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✨ 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

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🚀 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.

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🏁 Getting Started

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

pip install langtune

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 and src/langtune/cli/finetune.py.

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🐍 Supported Python Versions

• Python 3.8+

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🧩 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

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🏗️ 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

---
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.)

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🧩 Core Modules

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

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📊 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

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🔧 Advanced Configuration

LoRA Configuration

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

# 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
• 🎓 Tutorials and Examples
• 🔬 Research Papers
• 💡 Best Practices Guide
• 🐛 Troubleshooting

Research Papers

• LoRA: Low-Rank Adaptation of Large Language Models
• Attention Is All You Need
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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🧪 Testing & Quality

Run the comprehensive test suite:

# 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

# Linting
flake8 src/
black src/ --check

# Type checking
mypy src/

# Security scanning
bandit -r src/

---

🚀 Examples & Use Cases

Text Classification

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

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"
)

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🧩 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

# 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 - Ask questions and share ideas
• 🐛 Issue Tracker - Report bugs and request features
• 📖 Contributing Guide - Detailed contribution guidelines
• 🎯 Roadmap - See what's planned for future releases

📄 License & Citation

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use langtune in your research, please cite:

@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 - Deep learning framework
• HuggingFace - Transformers and model hub
• PEFT - Parameter-efficient fine-tuning methods

Made in India 🇮🇳 with ❤️ by the langtune team
Star ⭐ this repo if you find it useful!