Metadata-Version: 2.4
Name: metisos-arc-core
Version: 1.2.2
Summary: Adaptive Recursive Consciousness (ARC) Core - A framework for continual learning AI systems
Home-page: https://github.com/metisos/arc-core
Author: MetisOS
Author-email: cjohnson@metisos.com
Keywords: artificial intelligence,deep learning,transformers,continual learning,lora,neural networks,reasoning,memory
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
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: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: torch>=2.0.0
Requires-Dist: transformers>=4.25.0
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# ARC Core

Adaptive Recursive Consciousness (ARC) Core is a framework for building continual learning AI systems that can learn and reason over time. It features:

- **Continual Learning**: Real-time learning with LoRA adapters
- **Reasoning Engine**: Graph-based reasoning and pattern recognition
- **Biological Learning**: Implements biological learning mechanisms
- **Model Agnostic**: Works with various transformer architectures

## Installation

```bash
pip install arc-core
```

## Quick Start

```python
from arc_core import LearningARCConsciousness, ARCCore, ARCTrainer  # All aliases for the same class

# Initialize with a base model
arc = LearningARCConsciousness(model_name="gpt2")

# Learn from interactions
arc.learn_from_experience("The sky appears blue due to Rayleigh scattering")

# Generate responses
response = arc.generate("Why is the sky blue?")
print(response)
```

## Features

- **Dynamic LoRA Adapters**: Automatically adapts to different model architectures
- **Reasoning Graph**: Maintains a knowledge graph of learned concepts
- **Biological Learning**: Implements contextual gating and cognitive inhibition
- **Persistence**: Saves learning progress between sessions

## Documentation

For detailed documentation, see [ARC Core Documentation](https://github.com/yourusername/arc-core).

## License

MIT
