RL2T — Recursive Liquid-Lattice Text Architecture (v0.7.0)

Author: PlimbAi (plimb.ai.contact@gmail.com)
Package: rl2t

RL2T is a self-expanding, non-Transformer Deep Reinforcement Learning neural architecture equipped with:

• Multi-Scale YaRN-RoPE (ExpandableRotaryEmbedding) with Hippocampal Phase Precession & automatic width neurogenesis
• Thalamo-Cortical Global Workspace Bus (ThalamoCorticalGlobalWorkspace) integrating all cortical layers simultaneously
• Dual-Timescale Liquid State-Space Cells (LiquidTimeConstantCell) with RK2 second-order refinement, Targeted Neuron Mitosis, and Synaptic Consolidation
• Rotary Dual-Bank Holographic Associative Memory (HolographicAssociativeMemory)
• 4-Hormone Computational Endocrine System (NeuromodulatorSystem): Dopamine (DA), Acetylcholine (ACh), Norepinephrine (NE), and Serotonin (5-HT)
• Universal Multi-Format Transmuter (RL2TTransmuter): Import and surgically transmute any Transformer model (.gguf, .safetensors, .pt, .pth, .bin, .ckpt, .npz, .npy, HuggingFace folder, or PyTorch nn.Module) into RL2T!
• Autonomous Out-of-Training Growth: Train normally (fit_normal), then execute anywhere and watch the model continue growing its weights and architecture automatically (stop_growth=False), or lock its size at any time with stop_growth=True.

Installation

pip install rl2t

Quick Start

import torch
from rl2t import RL2TModel, RL2TTransmuter

# 1. Start from ANY scale ('femto', 'pico', 'nano', 'micro', 'small', 'medium', 'large', or exact parameter budget)
model = RL2TModel.from_any_scale("nano", vocab_size=256, stop_growth=False)

# 2. Or import & transmute ANY existing model (.gguf, .safetensors, .pt, .npz, or PyTorch nn.Module)
# model, report = RL2TTransmuter.from_any_file("model.gguf", stop_growth=False)

# 3. Normal supervised training (also calibrates internal RL Critic & World-Model)
batch = torch.randint(0, 256, (4, 32))
model.fit_normal(batch, epochs=5, lr=3e-3, allow_growth_during_training=False)

# 4. Execute outside training -> continues growing autonomously unless stop_growth=True!
out = model(batch[:2])
print("Current parameters:", model.count_parameters())

# 5. Lock growth whenever desired
model.set_growth_lock(stop_growth=True)

Contact & License

• Author: PlimbAi
• Email: plimb.ai.contact@gmail.com
• License: MIT