minilin 0.4.1
A universal low-resource deep learning framework - Learn More with Less
License: MIT
Requires Python: >=3.8
Keywords: deep-learning, machine-learning, low-resource, edge-ai, model-compression

# MiniLin Framework

**Learn More with Less** - A universal low-resource deep learning framework

[English](README.md) | [中文](README_cn.md) | [Русский](README_ru.md) | [Français](README_fr.md) | [العربية](README_ar.md)

[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Version](https://img.shields.io/badge/version-0.1.0-orange.svg)](https://github.com/alltobebetter/minilin)

## What is MiniLin?

MiniLin is a deep learning framework designed for **low-resource scenarios** where data is scarce and computational resources are limited. It provides an end-to-end automated workflow for text, image, and audio tasks, with built-in optimization for edge device deployment.

### Key Features

- **3-Line Solution**: Complete ML pipeline from data to deployment
- **Auto Strategy Selection**: Automatically chooses optimal training strategy based on data size
- **Lightweight Models**: Pre-integrated efficient models (DistilBERT, MobileNet, Wav2Vec2)
- **Model Compression**: Quantization, pruning, and knowledge distillation
- **Edge Deployment**: Export to ONNX, PyTorch, TFLite
- **Multi-Modal Support**: Text, image, and audio tasks
- **Few-Shot Learning**: LoRA, Adapter, and Prompt Tuning
- **Data Augmentation**: Back-translation, Mixup, CutMix, SpecAugment
- **API Deployment**: FastAPI REST API server

## Installation

### Basic Installation
```bash
pip install minilin
```

### With Optional Dependencies
```bash
# For vision tasks
pip install minilin[vision]

# For audio tasks
pip install minilin[audio]

# For optimization features
pip install minilin[optimization]

# For deployment
pip install minilin[deployment]

# Install everything
pip install minilin[all]
```

### From Source
```bash
git clone https://github.com/alltobebetter/minilin.git
cd minilin
pip install -e .
```

## Quick Start

### Basic Usage (3 lines!)
```python
from minilin import AutoPipeline

pipeline = AutoPipeline(task="text_classification", data_path="./data")
pipeline.train()
pipeline.deploy(output_path="./model.onnx")
```

### Advanced Usage
```python
from minilin import AutoPipeline

pipeline = AutoPipeline(
    task="text_classification",
    data_path="./data",
    target_device="mobile",
    max_samples=500,
    compression_level="high"
)

analysis = pipeline.analyze_data()
print(f"Strategy: {analysis['recommended_strategy']}")

pipeline.train(epochs=10, batch_size=16, learning_rate=2e-5)

metrics = pipeline.evaluate()
print(f"Accuracy: {metrics['accuracy']:.4f}")

pipeline.deploy(output_path="./model.onnx", quantization="int8")
```

## Advanced Features

### Few-Shot Learning with LoRA
```python
from minilin.models import apply_few_shot_method

model = apply_few_shot_method(model, method="lora", r=8, alpha=16)
pipeline.train(max_samples=50, epochs=20)
```

### Knowledge Distillation
```python
from minilin.optimization import KnowledgeDistiller

distiller = KnowledgeDistiller(
    teacher_model=large_model,
    student_model=small_model,
    temperature=3.0,
    alpha=0.5
)

metrics = distiller.distill(train_loader, val_loader, epochs=5)
```

### Multi-Modal Learning
```python
from minilin.models import create_multimodal_model

model = create_multimodal_model(
    text_model_name="distilbert-base-uncased",
    image_model_name="mobilenetv3_small_100",
    audio_model_name="facebook/wav2vec2-base",
    num_classes=10,
    fusion_method="attention"
)
```

### FastAPI Deployment
```python
from minilin.deployment import serve_model

serve_model(
    model_path="./model.onnx",
    task="text_classification",
    host="0.0.0.0",
    port=8000
)
```

## Supported Tasks

### Text Tasks
- Text Classification
- Named Entity Recognition (NER)
- Sentiment Analysis

### Vision Tasks
- Image Classification
- Image Augmentation (Mixup, CutMix)

### Audio Tasks
- Audio Classification
- Audio Augmentation (SpecAugment)

### Multi-Modal Tasks
- Text + Image
- Text + Audio
- Text + Image + Audio

## Core Modules

### Data Layer
- **DataAnalyzer**: Automatic data analysis and quality assessment
- **DataLoader**: Support for JSON, JSONL, CSV, TXT, directory formats
- **DataAugmenter**: Text augmentation (synonym, insertion, deletion, swap)
- **BackTranslator**: Back-translation (googletrans, DeepL)
- **ImageAugmenter**: Image augmentation (Mixup, CutMix, transforms)
- **AudioAugmenter**: Audio augmentation (noise, shift, speed, SpecAugment)

### Model Layer
- **ModelZoo**: Pre-integrated lightweight models
- **Trainer**: Text model trainer with auto hyperparameter tuning
- **ImageTrainer**: Image model trainer
- **AudioTrainer**: Audio model trainer
- **MultiModalModel**: Multi-modal fusion model
- **Few-Shot Learning**: LoRA, Adapter, Prompt Tuning

### Optimization Layer
- **Quantizer**: INT8/FP16 quantization
- **Pruner**: Structured/unstructured pruning
- **KnowledgeDistiller**: Teacher-Student distillation
- **ModelCompressor**: Compression orchestration

### Deployment Layer
- **ModelExporter**: Export to ONNX, PyTorch, TFLite
- **ModelServer**: FastAPI REST API server
- **Edge Optimization**: Mobile and embedded device support

## Performance

- **Training Speed**: 2-3x faster than standard training
- **Model Size**: Compressed to 10-20% of original size
- **Inference Speed**: Real-time on edge devices (>30 FPS)
- **Accuracy Loss**: <2% after compression

## Examples

Check out the [examples](examples/) directory:

- [Text Classification](examples/text_classification.py)
- [Sentiment Analysis](examples/sentiment_analysis.py)
- [Image Classification](examples/image_classification.py)
- [Audio Classification](examples/audio_classification.py)
- [Multi-Modal Learning](examples/multimodal_example.py)
- [Advanced Features](examples/advanced_features.py)

## Configuration

### Configuration File
Create `~/.minilin/config.json`:
```json
{
  "translation_api_key": "your-api-key",
  "huggingface_token": "your-token",
  "cache_dir": "~/.minilin/cache"
}
```

### Environment Variables
```bash
export MINILIN_TRANSLATION_API_KEY="your-api-key"
export MINILIN_HUGGINGFACE_TOKEN="your-token"
```

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact

- **GitHub**: https://github.com/alltobebetter/minilin
- **Email**: me@supage.eu.org

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Made with ❤️ by the MiniLin Team