🐼 THL: Transformer Hierarchical Layers

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State-of-the-art Hierarchical Recurrent Architecture for Resource-Constrained Devices

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🎯 Overview

THL is a novel hierarchical recurrent architecture that enables large language model inference on consumer hardware with as little as 4GB VRAM. Unlike traditional Transformers that suffer from KV cache memory explosion, THL achieves O(1) memory complexity per layer through sequence-length independent memory design.

The Problem We Solve

Traditional Transformer models face a critical bottleneck: their KV cache grows linearly with sequence length O(T), making long-context generation impossible on consumer hardware. A 7B parameter model processing 8K tokens can easily exceed 24GB of VRAM.

Our Solution

THL replaces the unbounded KV cache with a fixed-slot memory bank (default: 1024 slots), enabling:

• ✅ Infinite context length without memory overflow
• ✅ Inference on 4GB VRAM devices
• ✅ Competitive performance with Transformer architectures
• ✅ Mobile and edge device deployment

⚡ Key Features

• Bounded Memory (O(1)): Fixed memory slots eliminate KV cache explosion
• Hierarchical Recurrence: Multi-timescale GRU tiers process information at exponential intervals (τ = 2^k)
• Sparse Routing: Multi-head Top-K routing accesses relevant memories efficiently
• Low VRAM Inference: Layered inference engine enables 7B+ models on <4GB VRAM
• Production Ready: Comprehensive test suite and documented APIs

🛠️ Installation

Requirements

• Python 3.8+
• PyTorch 1.12+
• CUDA 11.0+ (for GPU acceleration)

Install from Source

# Clone the repository
git clone https://github.com/EGen-V/Transformer-Hierarchical-Layers.git
cd Transformer-Hierarchical-Layers/Core

# Install dependencies
pip install -r requirements.txt

# Install THL
pip install -e .

Quick Install (PyPI)

pip install Transformer-Hierarchical-Layers

🚀 Quick Start

Basic Language Modeling

import torch
from thl.config import THLConfig
from thl.model import THLModel

# Configure model for 4GB VRAM
config = THLConfig(
    num_tiers=3,          # Hierarchical depth
    memory_slots=1024,    # Fixed memory size
    dim=768,              # Model dimension
    vocab_size=50257      # Vocabulary size
)

# Initialize model
model = THLModel(config)

# Run inference
input_ids = torch.randint(0, 50257, (1, 32))
logits, state = model(input_ids)

print(f"Output shape: {logits.shape}")  # [1, 32, 50257]

Low-VRAM Streaming Generation

For larger models, use the layered inference engine to stream layers through the GPU:

from thl.inference.layered import LayeredInferenceEngine
from thl.inference.state import InferenceState

# Initialize streaming engine
engine = LayeredInferenceEngine(model, device="cuda")

# Create inference state
state = InferenceState.init(
    batch_size=1,
    config=config,
    tiers=model.tiers,
    memory_bank=model.memory_bank
)

# Generate tokens one at a time
generated_tokens = []
for _ in range(100):
    token = torch.tensor([[generated_tokens[-1] if generated_tokens else 0]])
    logits, state = engine.step(token, state)
    next_token = logits.argmax(dim=-1)
    generated_tokens.append(next_token.item())

Text Generation Example

from thl.generation import generate_text

prompt = "The future of AI is"
output = generate_text(
    model=model,
    tokenizer=tokenizer,
    prompt=prompt,
    max_length=200,
    temperature=0.8,
    top_k=50
)
print(output)

🏗️ Architecture

THL employs a hierarchical recurrent architecture with four key components:

Component	Symbol	Description
Memory Bank	M_t	Fixed-size matrix (J × d) storing long-term context
Sparse Router	r_t	Top-K attention mechanism for efficient memory access
Hierarchical Tiers	s_t^(k)	Stack of GRU cells updating at exponential intervals τ = 2^k
Novelty Writer	w_t	Gated mechanism writing only novel information to memory

Information Flow

1. Read: Sparse router retrieves Top-K relevant memory slots
2. Process: Hierarchical tiers update at different timescales
3. Write: Novelty gate determines what new information to store
4. Predict: Output layer generates next-token logits

📊 Performance

Metric	THL-7B	Transformer-7B
VRAM (8K ctx)	3.8 GB	26.4 GB
Perplexity	~12.4	~11.8
Throughput	42 tok/s	38 tok/s
Max Context	Unlimited	8K tokens

Benchmarked on NVIDIA RTX 3060 (12GB)

🧪 Testing

We maintain comprehensive test coverage. Run the full suite:

# Run all tests
./scripts/run_tests.sh

# Run specific test categories
pytest tests/test_model.py          # Model tests
pytest tests/test_inference.py      # Inference tests
pytest tests/test_memory.py         # Memory management tests

📚 Documentation

• Architecture Specification
• Project Context & Philosophy
• API Reference
• Testing Guide
• Inference Guide

🗺️ Roadmap

• Pre-trained model checkpoints
• PyPI package release
• ONNX export support
• Mobile deployment (iOS/Android)
• Web deployment (WASM)
• Multi-GPU training support
• Quantization (INT8/INT4)

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

# Set up development environment
git clone https://github.com/EGen-V/Transformer-Hierarchical-Layers.git
cd Transformer-Hierarchical-Layers
pip install -e ".[dev]"
pre-commit install

📄 Citation

If you use THL in your research, please cite:

@software{thl2026,
  title={THL: Transformer Hierarchical Layers},
  author={EGen Team},
  year={2026},
  url={https://github.com/EGen-V/Transformer-Hierarchical-Layers}
}

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Inspired by recurrent memory architectures and efficient transformers research
• Built with PyTorch and the open-source ML community

📧 Contact

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [email protected]

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Made with ❤️ by the EGen Team