capibara-ent 0.1.1

Un modelo de lenguaje avanzado que integra Mamba SSM, manejo multilingüe y características éticas

CapibaraENT CLI

CapibaraENT is a command-line tool for training, evaluating, and deploying Capibara-based language models, optimized for TPUs.

Features

• Training and evaluation of Capibara models
• Built-in TPU support
• Model deployment
• Performance measurement
• Docker container execution
• Model deserialization from JSON

Requirements

• Python 3.7+
• PyTorch 1.8+
• PyTorch/XLA
• Docker (optional, for container execution)

Installation

1. Clone this repository:

  git clone https://github.com/your-username/capibaraent-cli.git
  cd capibaraent-cli

1. Install dependencies:

   pip install -r requirements.txt

Usage

The CapibaraENT CLI offers various options for working with Capibara models:

python capibaraent_cli.py [options]

Available options:

• --log-level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
• --train: Train the model
• --evaluate: Evaluate the model
• --use-docker: Run the model inside Docker
• --deserialize-model: Deserialize the model from JSON
• --deploy: Deploy the model
• --measure-performance: Measure the model's performance
• --model: Path to the model JSON file (for deserialization)

Usage Examples

1. Train a model:

   python capibaraent_cli.py --train

2. Evaluate a model:

   python capibaraent_cli.py --evaluate

3. Deploy a model:

   python capibaraent_cli.py --deploy

5. Measure model performance:

   python capibaraent_cli.py --measure-performance
   

6. Run a model in Docker:

   python capibaraent_cli.py --use-docker
   

7. Deserialize and run a model from JSON:

   python capibaraent_cli.py --deserialize-model --model model.json

Configuration

Model configuration is handled through the core/config.py file. To modify the default settings, edit this file directly. Key configuration parameters include:

• input_dim
• batch_size
• learning_rate
• device_type

Example of core/config.py:

Development

To contribute to the project:

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

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Marco Durán - [e-mail]

Project Link: https://github.com/anachroni-io/capibaraent-cli