KRULL-Nano Simple

KRULL means Knowledge Running Under Lightweight Language.

KRULL-Nano is a lightweight decoder-only Small Language Model (SLM) architecture designed for embedded devices, edge AI, offline inference, and privacy-preserving applications.

Unlike cloud-oriented LLMs optimized for massive datacenters, KRULL-Nano is designed from the ground up for:

• low latency
• low memory usage
• deterministic inference
• offline execution
• edge sovereignty
• efficient deployment on constrained hardware

Architecture

KRULL-Nano uses a compact decoder-only transformer architecture with multiple optimizations for edge execution.

Core Components

Decoder-Only Transformer

• autoregressive causal language modeling
• GPT-style token prediction
• sreaming-friendly generation

RMSNorm

KRULL replaces LayerNorm with RMSNorm to:

• reduce computational overhead
• improve low-precision stability
• minimize memory bandwidth

Multi-Query Attention (MQA)

Instead of full multi-head attention:

• multiple query heads
• shared key/value heads

Benefits:

• reduced KV cache size
• faster inference
• lower RAM usage

Gated Feed Forward Network

KRULL uses a gated FFN inspired by modern efficient transformer architectures.

Benefits:

• improved parameter efficiency
• lower compute cost
• better expressivity per parameter

What is included

• Decoder-only GPT-style model
• RMSNorm
• Multi-query attention
• Gated feed-forward block
• Simple character tokenizer
• CPU training script
• Text generation script
• ONNX export script
• Windows-friendly imports

Project structure

krull_nano_simple/
├── krull/
│   ├── __init__.py
│   ├── model.py
│   └── tokenizer.py
├── scripts/
│   ├── train_tokenizer.py
│   ├── train_lm.py
│   ├── generate.py
│   └── export_onnx.py
├── configs/
│   └── krull_nano.json
├── data/
│   └── tiny_corpus.txt
├── artifacts/
├── requirements.txt
├── LICENSE
└── README.md

Setup on Windows

Open PowerShell or CMD:

cd C:\workspace\krull_nano_simple
python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt

Setup on Linux/macOS

cd krull_nano_simple
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

1. Train tokenizer

python scripts/train_tokenizer.py --input data/tiny_corpus.txt --out artifacts/tokenizer.json

2. Train model

python scripts/train_lm.py --config configs/krull_nano.json --tokenizer artifacts/tokenizer.json --data data/tiny_corpus.txt --out artifacts/krull_nano.pt --epochs 10 --device cpu

3. Generate text

python scripts/generate.py --model artifacts/krull_nano.pt --tokenizer artifacts/tokenizer.json --prompt "KRULL is" --device cpu

4. Export ONNX

python scripts/export_onnx.py --model artifacts/krull_nano.pt --out artifacts/krull_nano.onnx

Notes

This repo is for learning and experimentation. The default dataset is tiny, so the generated text will not be intelligent. Replace data/tiny_corpus.txt with a larger corpus to train a better model.

License

MIT