drzo/echoself

NanEcho — Deep Tree Echo Cognitive Model

Model Description

NanEcho is a transformer-based language model with iterative connection building, adaptive attention, and Deep Tree Echo cognitive architecture integration. It features persona dimensions (cognitive, introspective, adaptive, recursive) and hypergraph pattern recognition. This is the CI-mode checkpoint from the 9cog/echoself repository, trained using the agent-neuro-train supervised pipeline.

Architecture

Parameter	Value
Model Type	GPT-2 (causal LM)
Vocabulary Size	50,304
Embedding Dimension	256
Attention Heads	4
Transformer Layers	4
MLP Inner Dimension	1,024
Context Length	1,024
Dropout	0.1
Total Parameters	~24M

Training

Metric	Value
Training Mode	CI (Agent-Neuro supervised)
Training Iterations	200
Best Validation Loss	1.9258
Output Directory	out-nanecho-ci
Orchestrator	Agent-Neuro
Persona Enforced	Deep Tree Echo
Source Run	22276548709

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

model = GPT2LMHeadModel.from_pretrained("drzo/echoself")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

inputs = tokenizer("Echo Self is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Data

The model was trained on Echo Self documentation and cognitive architecture descriptions, including hypergraph reasoning patterns, persona dimension examples, and recursive introspection samples from the echoself.md corpus.

Limitations

This is an early CI-mode research checkpoint (200 iterations, 4 layers). It demonstrates the training pipeline but has not yet reached convergence. Full training runs with 8+ layers and 5000+ iterations are expected to produce significantly better results.

Source

Trained from the 9cog/echoself repository using the agent-neuro-train.yml GitHub Actions workflow with Deep Tree Echo persona enforcement.

Citation

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

More Information

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

License

AGPL-3.0