https://github.com/huyduc1602/generative_ai_project
🤖 A state-of-the-art generative AI model for creating realistic text/images with PyTorch. Includes training pipeline, evaluation metrics, and easy-to-use inference API.
https://github.com/huyduc1602/generative_ai_project
ai-research artificial-intelligence data-science deep-learning generative-ai machine-learning neural-networks python pytorch
Last synced: 3 months ago
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🤖 A state-of-the-art generative AI model for creating realistic text/images with PyTorch. Includes training pipeline, evaluation metrics, and easy-to-use inference API.
- Host: GitHub
- URL: https://github.com/huyduc1602/generative_ai_project
- Owner: huyduc1602
- Created: 2025-04-20T03:50:15.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-04-20T03:53:18.000Z (6 months ago)
- Last Synced: 2025-04-20T04:29:16.977Z (6 months ago)
- Topics: ai-research, artificial-intelligence, data-science, deep-learning, generative-ai, machine-learning, neural-networks, python, pytorch
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Generative AI Project
This repository contains the implementation of a generative AI model.
## About
This generative AI project implements a state-of-the-art deep learning model capable of generating [specific type of content - text/images/audio/etc]. Built on [framework - PyTorch/TensorFlow/etc], it leverages recent advancements in [specific techniques - transformer architectures/GANs/etc] to produce high-quality outputs with minimal computational resources.
### Key Features
- High-quality content generation with fine-tuned control parameters
- Efficient implementation optimized for both training and inference
- Comprehensive evaluation metrics to assess generation quality
- Scalable architecture that can be deployed in various environments
- Easy-to-use API for integration into other applications
### Technical Stack
- [List key libraries/frameworks used]
- [Mention any novel techniques implemented]
- [Note any specific architectural decisions]
### Use Cases
- [Primary use case]
- [Secondary use case]
- [Additional applications]
### Project Status
This project is currently in [development/beta/production] stage. [Optional: mention roadmap or upcoming features]
## Project Structure
```
generative_ai_project/
├── configs/ # Configuration files
├── data/ # Data directory
│ ├── raw/ # Raw, immutable data
│ └── processed/ # Processed data, ready for training
├── docs/ # Documentation
├── notebooks/ # Jupyter notebooks for exploration and visualization
├── src/ # Source code
│ ├── data/ # Data processing scripts
│ ├── models/ # Model architecture definitions
│ ├── training/ # Training scripts
│ ├── evaluation/ # Evaluation scripts
│ └── inference/ # Inference and serving code
└── tests/ # Unit tests
```
## Setup
1. Clone the repository
2. Install the dependencies:
```bash
pip install -r requirements.txt
```
3. Configure the project by editing the configuration files in the `configs/` directory
## Usage
### Data Preparation
```bash
python src/data/preprocess.py --config configs/data_config.yaml
```
### Model Training
```bash
python src/training/train.py --config configs/training_config.yaml
```
### Evaluation
```bash
python src/evaluation/evaluate.py --model_path models/model.pt --test_data data/processed/test.csv
```
### Inference
```bash
python src/inference/predict.py --model_path models/model.pt --input "Your input text here"
```
## License
This project is licensed under the MIT License - see the LICENSE file for details.