| --- |
| 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_20260425_135103_18000_22deff1b_9470fbb7 |
| - **Training Iteration**: 18000 |
| - **Validation Loss**: 0.00032569289276580094 |
| - **Quality Score**: 2764800.7811699593 |
| |
| ## 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 |
| |
