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: 12 layers, 768 embedding dimensions
• Vocabulary Size: 50257
• Context Length: N/A tokens

Training Details

• Checkpoint ID: unknown
• Training Iteration: N/A
• Validation Loss: N/A
• Quality Score: N/A

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

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

@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