Component	Description
EARCP Module	Sparse Compression + Gated Integration
LPOL Memory	9 domains with GQA
GQA	8 heads, 2 KV groups (75% savings)
EARCP Layers	8 layers × 6 experts
Neurogenesis	Dynamic growth (32-256 neurons)
Physics Prior	MDN with 8 components

📊 Training Results

Metric	Value
Epochs	6
Final Loss	0.0172
Coherence	~0.42
Neurogenesis Events	0
Parameters	227,991,690

🚀 Quick Start

python

import torch
from huggingface_hub import hf_hub_download

# Download and load
model_path = hf_hub_download(repo_id="amewebstudio/nexus-worldmodel-v2", filename="nexus_worldmodel_v2.pt")
checkpoint = torch.load(model_path, map_location="cuda")

config = checkpoint['config']
state_dict = checkpoint['model']

print(f"Epochs: {checkpoint['epochs']}")
print(f"Loss: {checkpoint['loss']:.4f}")

📁 Files

File	Description
`nexus_worldmodel_v2.pt`	Full checkpoint
`pytorch_model.bin`	Weights only
`config.json`	Model configuration
`cognitive_state.json`	Dynamic cognitive state
`configuration_nexus_worldmodel.py`	Config class
`model_index.json`	Component index

⚠️ Dynamic Architecture

This model uses a cognitive-dynamic architecture where:

Expert count per layer can grow during training
Neuron count can change (neurogenesis)
Memory states are persistent

When loading, use strict=False to handle potential size mismatches:

model.load_state_dict(state_dict, strict=False)

📖 Configuration

json

{
  "d_model": 512,
  "n_layers": 8,
  "latent_dim": 256,
  "use_gqa": true,
  "gqa_num_kv_groups": 2,
  "neurogenesis_enabled": true
}

👤 Author

Mike Amega (Logo) - Ame Web Studio

📄 License

Apache 2.0