# RETNETS (Retentive Networks) - PyPI Package
## Overview
RETNETS is a powerful package designed to facilitate the usage of the Retentive Network by AI engineers. Whether you're working with Torch or Tensorflow, RETNET provides seamless integration and empowers you to harness the potential of Retentive Network for your AI projects.
## What is the Retentive Network?
## What is RETNET?
RETNETS, or Retentive Network, is a revolutionary neural network architecture transforming large language models. It introduces the "retention mechanism," efficiently addressing inference and parallelism challenges faced by traditional Transformers.
The retention mechanism utilizes parallel, recurrent, and chunkwise recurrent representations, overcoming the "impossible triangle" challenge. RETNET demonstrates remarkable performance and efficiency, enabling faster decoding and significant memory savings compared to standard Transformers.
The future of RETNET looks promising, with plans for scaling and deployment on various edge devices. It promises to revolutionize large language models, empowering AI engineers with efficient training and accelerated inference capabilities. Embrace RETNET for cutting-edge advancements in natural language processing!
## Features
- Easy-to-use interface for both Torch and Tensorflow.
- Simple and intuitive integration with your existing AI projects.
- Harness the power of the Retentive Network for sequential data processing.
- Comprehensive documentation and code examples to get you started quickly.
## Installation
You can install RETNETS using pip with the following command:
```bash
pip install retnets
```
## Usage
Using RETNETS in your AI project is a breeze! Just import the package and start leveraging the capabilities of the Retentive Network.
```python
import retnets
# Your code here...
```
## Documentation
For detailed information on how to use RETNET with Torch or Tensorflow, please refer to our [documentation](#).
## Contributing
We welcome contributions from the community! If you find a bug, have a feature request, or want to add improvements, feel free to open an issue or submit a pull request.
## License
**Note:**
Some code is from the official Microsoft/Meta repository for Torchscale.
RETNETS is released under the [MIT License](https://opensource.org/licenses/MIT).
Raw data
{
"_id": null,
"home_page": "",
"name": "retnets",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "retnet,retnets,retentive networks,LLM,large language models,ai,artificial intelligence",
"author": "useCallback (Khalfoun Mohamed El Mehdi)",
"author_email": "<contact.khalfoun@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/5f/21/f9e5e352c8728c7d2cd6c5ae5aed0e17121f826d852b7c9c170dcacd9f11/retnets-0.0.2.tar.gz",
"platform": null,
"description": "\n# RETNETS (Retentive Networks) - PyPI Package\n\n## Overview\n\nRETNETS is a powerful package designed to facilitate the usage of the Retentive Network by AI engineers. Whether you're working with Torch or Tensorflow, RETNET provides seamless integration and empowers you to harness the potential of Retentive Network for your AI projects.\n\n## What is the Retentive Network?\n\n## What is RETNET?\n\nRETNETS, or Retentive Network, is a revolutionary neural network architecture transforming large language models. It introduces the \"retention mechanism,\" efficiently addressing inference and parallelism challenges faced by traditional Transformers.\n\nThe retention mechanism utilizes parallel, recurrent, and chunkwise recurrent representations, overcoming the \"impossible triangle\" challenge. RETNET demonstrates remarkable performance and efficiency, enabling faster decoding and significant memory savings compared to standard Transformers.\n\nThe future of RETNET looks promising, with plans for scaling and deployment on various edge devices. It promises to revolutionize large language models, empowering AI engineers with efficient training and accelerated inference capabilities. Embrace RETNET for cutting-edge advancements in natural language processing!\n\n## Features\n\n- Easy-to-use interface for both Torch and Tensorflow.\n- Simple and intuitive integration with your existing AI projects.\n\n- Harness the power of the Retentive Network for sequential data processing.\n\n- Comprehensive documentation and code examples to get you started quickly.\n\n## Installation\n\nYou can install RETNETS using pip with the following command:\n\n```bash\npip install retnets\n```\n\n## Usage\n\nUsing RETNETS in your AI project is a breeze! Just import the package and start leveraging the capabilities of the Retentive Network.\n\n```python\nimport retnets\n\n# Your code here...\n```\n\n## Documentation\n\nFor detailed information on how to use RETNET with Torch or Tensorflow, please refer to our [documentation](#).\n\n## Contributing\n\nWe welcome contributions from the community! If you find a bug, have a feature request, or want to add improvements, feel free to open an issue or submit a pull request.\n\n## License\n\n**Note:**\nSome code is from the official Microsoft/Meta repository for Torchscale.\n\nRETNETS is released under the [MIT License](https://opensource.org/licenses/MIT).\n",
"bugtrack_url": null,
"license": "",
"summary": "RETNETS is a powerful package designed to facilitate the usage of the Retentive Network by AI engineers. Whether you are working with Torch or Tensorflow, RETNET provides seamless integration and empowers you to harness the potential of Retentive Network for your AI projects.",
"version": "0.0.2",
"project_urls": {
"Download": "https://github.com/useCallback/retnets.git",
"github": "https://github.com/useCallback/retnets.git"
},
"split_keywords": [
"retnet",
"retnets",
"retentive networks",
"llm",
"large language models",
"ai",
"artificial intelligence"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "b3c2a4d6cefdb0bb87dadb06d8fc50f61e194229225dbafbc09f58700d7b04de",
"md5": "6009dfe216cc159bba226ab3f1f5f045",
"sha256": "22998dfa9ab28f54dca4a23e5c1781707a1ece40a9f64b00b9b2d6180c2e2687"
},
"downloads": -1,
"filename": "retnets-0.0.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6009dfe216cc159bba226ab3f1f5f045",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 2584,
"upload_time": "2023-07-29T19:05:25",
"upload_time_iso_8601": "2023-07-29T19:05:25.266628Z",
"url": "https://files.pythonhosted.org/packages/b3/c2/a4d6cefdb0bb87dadb06d8fc50f61e194229225dbafbc09f58700d7b04de/retnets-0.0.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "5f21f9e5e352c8728c7d2cd6c5ae5aed0e17121f826d852b7c9c170dcacd9f11",
"md5": "9e959692cce29055a541f71e27b33da7",
"sha256": "f469008d93f15065631193c802ec10ac4becc973a2274876bfc49cd008f6c658"
},
"downloads": -1,
"filename": "retnets-0.0.2.tar.gz",
"has_sig": false,
"md5_digest": "9e959692cce29055a541f71e27b33da7",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 2838,
"upload_time": "2023-07-29T19:05:27",
"upload_time_iso_8601": "2023-07-29T19:05:27.166938Z",
"url": "https://files.pythonhosted.org/packages/5f/21/f9e5e352c8728c7d2cd6c5ae5aed0e17121f826d852b7c9c170dcacd9f11/retnets-0.0.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-07-29 19:05:27",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "useCallback",
"github_project": "retnets",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "retnets"
}
JSON
