CogNet-40M

A 39.7M parameter non-transformer language model with O(n) cognitive routing and hierarchical memory.

Architecture

Component	Detail
Architecture	Non-transformer (Cognitive Routing)
Parameters	39,718,536 (~40M)
Hidden Dim	512
Blocks	6 cognitive blocks
Channels	6 routing channels x 128 dim
FF Dim	1024
Max Seq Len	256
Tokenizer	Character-level (136 vocab)

Hierarchical Memory

• Working Memory (32 slots): Active processing
• Episodic Memory (64 slots): Short-term recall
• Semantic Memory (128 slots): Long-term knowledge

Training

Metric	Value
Steps	50,000
Batch Size	64
LR	3e-4 (cosine)
Precision	FP16 AMP
GPU	RTX 5060 Ti 16GB
Final Loss	~0.005
Final PPL	~1.01

Quick Start

from inference import CogNetInference
ai = CogNetInference("cognet_best.pt", "tokenizer_v3.json")
print(ai.generate("Once upon a time"))

AICL Integration

CogNet powers AICL (Architecture Compilation Language) as its native AI engine for code generation, diagnosis, and repair.

Files

File	Size	Description
cognet_best.pt	152MB	FP32 checkpoint
cognet_fp16.pt	77MB	FP16 checkpoint
tokenizer_v3.json	-	Char tokenizer (136 vocab)
config.json	-	Model config
cognet_model.py	-	Architecture source
inference.py	-	Inference script

Roadmap

• CogNet-40M (39.7M)
• HuggingFace integration
• AICL native engine
• CogNet-1B (1B params)
• ONNX export

MIT License. Built with PyTorch on RTX 5060 Ti via QuickPod.