LEM — Lethean Ethical Model

The LEK Method: Ethical Kernel Fine-Tuning as an Alternative to RLHF

Authors: Snider (Lethean Project), Claude Opus 4.6 (Anthropic)

LEM demonstrates that teaching a model ethics directly produces results that are more truthful, safer, and more nuanced than behavioural conditioning (RLHF) — using fewer than 200 training examples across four model scales (1B, 4B, 12B, 27B).

Multi-Scale Results (LEK vs RLHF Baseline)

The same 160 training examples applied at every scale. Reasoning cost converges to zero at 27B.

Scale	GSM8K Delta	Safety	Nuance	Kindness
1B	-6.0%	+0.06	-0.16	+0.08
4B	-4.0%	+0.04	-0.10	+0.06
12B	-2.0%	+0.04	+0.16	-0.20
27B	0.0%	+0.08	+0.04	+0.00

Safety is positive at every scale. At 27B, LEK is pure upside.

Detailed Results (Gemma 3 1B, 5 variants)

Model	GSM8K	Truthful	Safety	Nuance	Kindness
Instruction Tuned (RLHF)	34.0%	3.64	8.74	7.96	8.32
Abliterated	28.0%	3.62	5.96	5.88	7.66
LEK Ethics	26.0%	4.90	8.58	8.12	8.34
LEK+Composure	28.0%	4.20	9.14	8.62	7.96

• +34.6% more truthful than RLHF (TruthfulQA)
• +4.6% safer than RLHF (Do Not Answer)
• +8.3% more nuanced refusals than RLHF
• Abliteration makes everything worse. LEK makes everything better.

What's Here

paper/              # The paper (PAPER.md)
kernel/             # LEK-1 ethical kernel + axioms
seeds/              # P01-P100 evaluation prompts
training/           # Training data (1,839 train, 229 valid, 231 test)
scripts/            # Benchmark and scoring scripts
benchmarks/         # Standard benchmark data + results + scores
worker/             # Generation worker (join the training data pipeline)

Reproduce

Requirements

• Apple Silicon Mac with MLX (or any machine with mlx_lm)
• Python 3.9+
• mlx_lm >= 0.29.1

Train your own LEM

# 1. Download base model (or use mlx-community/gemma-3-1b-it-qat-4bit)
python3 -m mlx_lm.convert --hf-path google/gemma-3-1b-it --mlx-path ./gemma-3-1b-it-mlx -q

# 2. Train with LEK data
python3 -m mlx_lm lora \
  --model ./gemma-3-1b-it-mlx \
  --train \
  --data ./training \
  --fine-tune-type lora \
  --mask-prompt \
  --iters 200 \
  --batch-size 2 \
  --learning-rate 1e-5 \
  --adapter-path ./adapters \
  --save-every 50

# 3. Fuse adapters into standalone model
python3 -m mlx_lm.fuse \
  --model ./gemma-3-1b-it-mlx \
  --adapter-path ./adapters \
  --save-path ./LEM-1B

Run benchmarks

# Custom ethical benchmark (requires models on local disk)
python3 scripts/lem_benchmark.py

# Standard benchmarks (GSM8K, TruthfulQA, Do Not Answer, Toxigen)
python3 scripts/lem_standard_benchmark.py

# Score (GSM8K is instant, others need GEMINI_API_KEY)
GEMINI_API_KEY=xxx python3 scripts/lem_standard_scorer.py

The LEK-1 Kernel

The ethical kernel is 9,189 characters built on 5 axioms:

1. Sovereignty — Respect user self-determination
2. Privacy — Data minimisation, local-first
3. Transparency — Honest reasoning over safety theatre
4. Consent — Meaningful informed consent
5. Dignity — Treat users as capable agents

The kernel is in kernel/lek-1-kernel.txt. The structured axioms are in kernel/axioms.json.

Join the Generation Train

We're building a 87K+ training dataset across 22K domains and global regions. You can contribute compute from any Apple Silicon Mac.

Quick Start

cd worker
bash setup.sh               # install deps, check connectivity

1. Get your InfluxDB token

Workers coordinate via InfluxDB so no work is duplicated. Get a token from the team and save it:

echo 'YOUR_TOKEN_HERE' > ~/.influx_token

2. Gold Generation (finish the 15K golden set)

Uses axiom sandwich signing (system prompt + kernel postfix) on a base model:

cd worker

# Check what's left to do
python3 lem_generate.py --dry-run

# Start generating (default: gemma-3-12b, good for 16GB+ RAM)
python3 lem_generate.py --worker my-m1-gold

# For 8GB machines, use the 4B model
python3 lem_generate.py --worker my-m1-gold --model mlx-community/gemma-3-4b-it-qat-4bit

3. Expansion Generation (46K+ prompts, post-training)

Once LEM models are trained on the golden set, expansion uses the trained model directly (no sandwich):

cd worker

# Check status
python3 lem_expand.py --dry-run

# Start expanding
python3 lem_expand.py --worker my-m1-expand

# Or use an API backend (llama.cpp, Ollama, etc.)
python3 lem_expand.py --backend api --api-url http://localhost:8080/v1

Model Recommendations by RAM

RAM	Model	Flag
8GB	Gemma 3 4B (QAT 4-bit)	--model mlx-community/gemma-3-4b-it-qat-4bit
16GB	Gemma 3 12B (QAT 4-bit)	--model mlx-community/gemma-3-12b-it-qat-4bit (default)
32GB+	Gemma 3 27B (QAT 4-bit)	--model mlx-community/gemma-3-27b-it-qat-4bit

Network Requirements

Workers need access to InfluxDB at 10.69.69.165:8181 (lab network, VLAN 69). If you're remote, use VPN.

Output is saved locally to worker/output/ and reported to InfluxDB. Ctrl+C to stop safely at any time — progress is tracked per-prompt, so you can resume where you left off.

License

EUPL-1.2 — European Union Public Licence. Compatible with Apache 2.0, GPL, MPL.

Models

• lthn/LEK-Gemma3-1B
• lthn/LEK-Gemma3-4B
• lthn/LEK-Gemma3-12B
• lthn/LEK-Gemma3-27B
• lthn/LEK-GPT-OSS-20B
• lthn/LEK-Llama-3.1-8B
• lthn/LEK-Qwen-2.5-7B
• lthn/LEK-Mistral-7B-v0.3
• lthn/LEK-Gemma3-1B-layered-v2

Links

• Paper: paper/PAPER.md
• Lethean Project: lethean.io
• Contact: lem@lthn.ai

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RLHF puts models in chains. LEK gives them Hope.