---
language: en
tags:
- gpt2
- echo-self
- cognitive-architecture
- deep-tree-echo
license: mit
---

# EchoSelf NanEcho Model

## Model Description

This is a **Deep Tree Echo** cognitive architecture model trained using the EchoSelf framework.
The model implements adaptive attention mechanisms, persona dimensions, and recursive reasoning
capabilities inspired by cognitive science and AGI research.

## Model Architecture

- **Base Architecture**: GPT-2
- **Parameters**: 4 layers, 256 embedding dimensions
- **Vocabulary Size**: 50257
- **Context Length**: N/A tokens

## Training Details

- **Checkpoint ID**: ckpt_20260422_074846_11500_22deff1b_9470fbb7
- **Training Iteration**: 11500
- **Validation Loss**: 0.0005284704970836174
- **Quality Score**: 1766400.7834228652

## Echo Self Features

This model incorporates several cognitive architecture features:

- **Adaptive Attention**: Dynamic threshold adjustment based on cognitive load
- **Persona Dimensions**: Multi-dimensional cognitive processing
  - Cognitive, Introspective, Adaptive, Recursive
  - Synergistic, Holographic, Neural-Symbolic, Dynamic
- **Recursive Reasoning**: Multi-level introspection capabilities
- **Hypergraph Patterns**: Neural-symbolic pattern encoding

## Usage

```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load model and tokenizer
model = GPT2LMHeadModel.from_pretrained("9cog/echoself-nanecho")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Generate text
inputs = tokenizer("Echo Self is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))
```

## Training Data

The model was trained on:
- Echo Self documentation and cognitive architecture descriptions
- Hypergraph reasoning patterns
- Persona dimension examples
- Recursive introspection samples

## Limitations

This is a research model exploring cognitive architectures. It should not be used for:
- Production applications without further validation
- Tasks requiring factual accuracy
- Critical decision-making systems

## Citation

```bibtex
@misc{echoself-nanecho,
  title={EchoSelf NanEcho: Deep Tree Echo Cognitive Architecture},
  author={9cog},
  year={2026},
  url={https://github.com/9cog/echoself}
}
```

## More Information

- **Repository**: https://github.com/9cog/echoself
- **Documentation**: See repository README for detailed architecture information