Metadata-Version: 2.4
Name: gptmed
Version: 0.1.2
Summary: A lightweight GPT-based language model framework for training custom question-answering models on any domain
Author-email: Sanjog Sigdel <sigdelsanjog@gmail.com>
Maintainer-email: Sanjog Sigdel <sigdelsanjog@gmail.com>
License: MIT License
        
        Copyright (c) 2026 Your Name
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/sigdelsanjog/gptmed
Project-URL: Documentation, https://github.com/sigdelsanjog/gptmed#readme
Project-URL: Repository, https://github.com/sigdelsanjog/gptmed
Project-URL: Issues, https://github.com/sigdelsanjog/gptmed/issues
Keywords: nlp,language-model,transformer,gpt,pytorch,qa,question-answering,training,deep-learning,custom-model
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: sentencepiece>=0.1.99
Requires-Dist: numpy>=1.24.0
Requires-Dist: tqdm>=4.65.0
Requires-Dist: pyyaml>=6.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: black>=22.0.0; extra == "dev"
Requires-Dist: flake8>=4.0.0; extra == "dev"
Requires-Dist: mypy>=0.950; extra == "dev"
Provides-Extra: training
Requires-Dist: tensorboard>=2.10.0; extra == "training"
Requires-Dist: wandb>=0.13.0; extra == "training"
Dynamic: license-file

# GptMed 🤖

A lightweight GPT-based language model framework for training custom question-answering models on any domain. This package provides a transformer-based GPT architecture that you can train on your own Q&A datasets - whether it's casual conversations, technical support, education, or any other domain.

[![PyPI version](https://badge.fury.io/py/gptmed.svg)](https://badge.fury.io/py/gptmed)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 📖 [Complete User Manual](USER_MANUAL.md) | [Quick Start](#quick-start)

> **New to GptMed?** Check out the [**step-by-step User Manual**](USER_MANUAL.md) for a complete guide on training your own model!

## Features

- 🧠 **Custom GPT Architecture**: Lightweight transformer model for any Q&A domain
- 🎯 **Domain-Agnostic**: Train on any question-answering dataset (casual chat, tech support, education, etc.)
- ⚡ **Fast Inference**: Optimized for quick question answering
- 🔧 **Flexible Training**: Easy to train on your own custom datasets
- 📦 **Lightweight**: Small model size suitable for edge deployment
- 🛠️ **Complete Toolkit**: Includes tokenizer training, model training, and inference utilities

## Table of Contents

- [Features](#features)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Package Structure](#package-structure)
  - [Core Modules](#core-modules)
  - [Model Components](#model-components)
  - [Training Components](#training-components)
  - [Inference Components](#inference-components)
  - [Data Processing](#data-processing)
  - [Utilities](#utilities)
- [Model Architecture](#model-architecture)
- [Configuration](#configuration)
- [Documentation](#documentation)
- [Performance](#performance)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)
- [Support](#support)

## Installation

### From PyPI (Recommended)

```bash
pip install gptmed
```

### From Source

```bash
git clone https://github.com/sigdelsanjog/gptmed.git
cd gptmed
pip install -e .
```

### With Optional Dependencies

```bash
# For development
pip install gptmed[dev]

# For training
pip install gptmed[training]

# All dependencies
pip install gptmed[dev,training]
```

## Quick Start

### Inference (Generate Answers)

```python
from gptmed.inference.generator import TextGenerator
from gptmed.model.architecture import GPTTransformer
from gptmed.model.configs.model_config import get_small_config

# Load model
config = get_small_config()
model = GPTTransformer(config)

# Load your trained checkpoint
# model.load_state_dict(torch.load('path/to/checkpoint.pt'))

# Create generator
generator = TextGenerator(
    model=model,
    tokenizer_path='path/to/tokenizer.model'
)

# Generate answer
question = "What's your favorite programming language?"
answer = generator.generate(
    prompt=question,
    max_length=100,
    temperature=0.7
)

print(f"Q: {question}")
print(f"A: {answer}")
```

### Using Command Line

```bash
# Generate answers
gptmed-generate --prompt "How do I train a custom model?" --max-length 100

# Train model
gptmed-train --model-size small --num-epochs 10 --batch-size 16
```

### Training Your Own Model

```python
from gptmed.training.train import main
from gptmed.configs.train_config import get_default_config
from gptmed.model.configs.model_config import get_small_config

# Configure training
train_config = get_default_config()
train_config.batch_size = 16
train_config.num_epochs = 10
train_config.learning_rate = 3e-4

# Start training
main()
```

## Model Architecture

The model uses a custom GPT-based transformer architecture:

- **Embedding**: Token + positional embeddings
- **Transformer Blocks**: Multi-head self-attention + feed-forward networks
- **Parameters**: ~10M (small), ~50M (medium)
- **Context Length**: 512 tokens
- **Vocabulary**: Custom SentencePiece tokenizer trained on your data

## Configuration

### Model Sizes

```python
from gptmed.model.configs.model_config import (
    get_tiny_config,   # ~2M parameters - for testing
    get_small_config,  # ~10M parameters - recommended
    get_medium_config  # ~50M parameters - higher quality
)
```

### Training Configuration

```python
from gptmed.configs.train_config import TrainingConfig

config = TrainingConfig(
    batch_size=16,
    learning_rate=3e-4,
    num_epochs=10,
    warmup_steps=100,
    grad_clip=1.0
)
```

## Package Structure

### Core Modules

The `gptmed` package contains the following main modules:

```
gptmed/
├── model/                  # Model architecture and configurations
├── inference/              # Text generation and sampling
├── training/               # Training loops and datasets
├── tokenizer/              # Tokenizer training and data processing
├── data/                   # Data parsers and formatters
├── configs/                # Training configurations
└── utils/                  # Utilities (checkpoints, logging)
```

### Model Components

**`gptmed.model.architecture`** - GPT Transformer Implementation

- `GPTTransformer` - Main model class
- `TransformerBlock` - Individual transformer layers
- `MultiHeadAttention` - Attention mechanism
- `FeedForward` - Feed-forward networks
- `RoPEPositionalEncoding` - Rotary position embeddings

**`gptmed.model.configs`** - Model Configurations

- `get_tiny_config()` - ~2M parameters (testing)
- `get_small_config()` - ~10M parameters (recommended)
- `get_medium_config()` - ~50M parameters (high quality)
- `ModelConfig` - Custom configuration class

### Training Components

**`gptmed.training`** - Training Pipeline

- `train.py` - Main training script (CLI: `gptmed-train`)
- `Trainer` - Training loop with checkpointing
- `TokenizedDataset` - PyTorch dataset for tokenized data
- `create_dataloaders()` - DataLoader creation utilities

**`gptmed.configs`** - Training Configurations

- `TrainingConfig` - Training hyperparameters
- `get_default_config()` - Default training settings
- `get_quick_test_config()` - Fast testing configuration

### Inference Components

**`gptmed.inference`** - Text Generation

- `TextGenerator` - Main generation class
- `generator.py` - CLI command (CLI: `gptmed-generate`)
- `sampling.py` - Sampling strategies (top-k, top-p, temperature)
- `decoding_utils.py` - Decoding utilities
- `GenerationConfig` - Generation parameters

### Data Processing

**`gptmed.tokenizer`** - Tokenizer Training & Data Processing

- `train_tokenizer.py` - Train SentencePiece tokenizer
- `tokenize_data.py` - Convert text to token sequences
- SentencePiece BPE tokenizer support

**`gptmed.data.parsers`** - Data Parsing & Formatting

- `MedQuADParser` - XML Q&A parser (example)
- `CausalTextFormatter` - Format Q&A pairs for training
- `FormatConfig` - Formatting configuration

### Utilities

**`gptmed.utils`** - Helper Functions

- `checkpoints.py` - Model checkpoint management
- `logging.py` - Training metrics logging

---

## Detailed Project Structure

```
gptmed/
├── model/
│   ├── architecture/
│   │   ├── gpt.py              # GPT transformer model
│   │   ├── attention.py        # Multi-head attention
│   │   ├── feedforward.py      # Feed-forward networks
│   │   └── embeddings.py       # Token + positional embeddings
│   └── configs/
│       └── model_config.py     # Model size configurations
├── inference/
│   ├── generator.py            # Text generation (CLI command)
│   ├── sampling.py             # Sampling strategies
│   ├── decoding_utils.py       # Decoding utilities
│   └── generation_config.py    # Generation parameters
├── training/
│   ├── train.py                # Main training script (CLI command)
│   ├── trainer.py              # Training loop
│   ├── dataset.py              # PyTorch dataset
│   └── utils.py                # Training utilities
├── tokenizer/
│   ├── train_tokenizer.py      # Train SentencePiece tokenizer
│   └── tokenize_data.py        # Tokenize text data
├── data/
│   └── parsers/
│       ├── medquad_parser.py   # Example XML parser
│       └── text_formatter.py   # Q&A text formatter
├── configs/
│   └── train_config.py         # Training configurations
└── utils/
    ├── checkpoints.py          # Model checkpointing
    └── logging.py              # Training logging
```

### Command-Line Interface

The package provides two main CLI commands:

```bash
# Train a model
gptmed-train --model-size small --num-epochs 10 --batch-size 16

# Generate text
gptmed-generate --prompt "Your question?" --max-length 100
```

## Requirements

- Python >= 3.8
- PyTorch >= 2.0.0
- sentencepiece >= 0.1.99
- numpy >= 1.24.0
- tqdm >= 4.65.0

## Documentation

📚 **[Complete User Manual](USER_MANUAL.md)** - Step-by-step guide for training your own model

### Quick Links

- [User Manual](USER_MANUAL.md) - **Start here!** Complete training pipeline guide
- [Architecture Guide](ARCHITECTURE_EXTENSION_GUIDE.md) - Understanding the model architecture
- [Deployment Guide](DEPLOYMENT_GUIDE.md) - Publishing to PyPI
- [Changelog](CHANGELOG.md) - Version history

## Performance

| Model Size | Parameters | Training Time | Inference Speed |
| ---------- | ---------- | ------------- | --------------- |
| Tiny       | ~2M        | 2 hours       | ~100 tokens/sec |
| Small      | ~10M       | 8 hours       | ~80 tokens/sec  |
| Medium     | ~50M       | 24 hours      | ~50 tokens/sec  |

_Tested on GTX 1080 8GB_

## Examples

### Medical Question Answering

```python
# Example 1: Symptoms inquiry
question = "What are the early signs of Alzheimer's disease?"
answer = generator.generate(question, temperature=0.7)

# Example 2: Treatment information
question = "How is Type 2 diabetes treated?"
answer = generator.generate(question, temperature=0.6)

# Example 3: Medical definitions
question = "What is hypertension?"
answer = generator.generate(question, temperature=0.5)
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

## Citation

If you use this model in your research, please cite:

```bibtex
@software{llm_med_2026,
  author = {Sanjog Sigdel},
  title = {GptMed: A custom causal question answering general purpose GPT Transformer Architecture Model},
  year = {2026},
  url = {https://github.com/sigdelsanjog/gptmed}
}
```

## License

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

## Acknowledgments

- MedQuAD dataset creators
- PyTorch team

## Disclaimer

⚠️ **Medical Disclaimer**: This model is for research and educational purposes only. It should NOT be used for actual medical diagnosis or treatment decisions. Always consult qualified healthcare professionals for medical advice.

## Support

- � **[User Manual](USER_MANUAL.md)** - Complete step-by-step training guide
- �📫 Issues: [GitHub Issues](https://github.com/sigdelsanjog/gptmed/issues)
- 💬 Discussions: [GitHub Discussions](https://github.com/sigdelsanjog/gptmed/discussions)
- 📧 Email: sanjog.sigdel@ku.edu.np

## Changelog

See [CHANGELOG.md](CHANGELOG.md) for version history.

---

Made with ❤️ for learning purpose
