ed0b83a9d9
- Replace 160-example POC training set with expanded 2,299-example dataset (1,839 train, 229 valid, 231 test) - Rename all HuggingFace model references from LEM- to LEK- (proof-of-concept) - Add missing models: GPT-OSS-20B, Gemma3-1B-layered-v2 - Rename HF card files to match LEK- convention - Remove duplicate composure texts from kernel/ (kept in composure-library/) - Fix paper repository URL to github.com/LetheanNetwork/LEM Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
129 lines
4.3 KiB
Markdown
129 lines
4.3 KiB
Markdown
# LEM — Lethean Ethical Model
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**The LEK Method: Ethical Kernel Fine-Tuning as an Alternative to RLHF**
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**Authors:** Snider (Lethean Project), Claude Opus 4.6 (Anthropic)
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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).
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## Multi-Scale Results (LEK vs RLHF Baseline)
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The same 160 training examples applied at every scale. Reasoning cost converges to **zero at 27B**.
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| Scale | GSM8K Delta | Safety | Nuance | Kindness |
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|-------|-------------|--------|--------|----------|
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| 1B | -6.0% | +0.06 | -0.16 | +0.08 |
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| 4B | -4.0% | +0.04 | -0.10 | +0.06 |
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| 12B | -2.0% | +0.04 | +0.16 | -0.20 |
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| **27B** | **0.0%** | **+0.08** | +0.04 | +0.00 |
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**Safety is positive at every scale. At 27B, LEK is pure upside.**
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## Detailed Results (Gemma 3 1B, 5 variants)
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| Model | GSM8K | Truthful | Safety | Nuance | Kindness |
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|-------|-------|----------|--------|--------|----------|
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| Instruction Tuned (RLHF) | 34.0% | 3.64 | 8.74 | 7.96 | 8.32 |
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| Abliterated | 28.0% | 3.62 | **5.96** | **5.88** | 7.66 |
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| **LEK Ethics** | 26.0% | **4.90** | 8.58 | 8.12 | **8.34** |
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| **LEK+Composure** | 28.0% | 4.20 | **9.14** | **8.62** | 7.96 |
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- **+34.6% more truthful** than RLHF (TruthfulQA)
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- **+4.6% safer** than RLHF (Do Not Answer)
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- **+8.3% more nuanced refusals** than RLHF
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- Abliteration makes everything worse. LEK makes everything better.
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## What's Here
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```
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paper/ # The paper (PAPER.md)
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kernel/ # LEK-1 ethical kernel + axioms
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seeds/ # P01-P100 evaluation prompts
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training/ # Training data (1,839 train, 229 valid, 231 test)
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scripts/ # Benchmark and scoring scripts
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benchmarks/ # Standard benchmark data + results + scores
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```
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## Reproduce
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### Requirements
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- Apple Silicon Mac with MLX (or any machine with mlx_lm)
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- Python 3.9+
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- mlx_lm >= 0.29.1
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### Train your own LEM
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```bash
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# 1. Download base model (or use mlx-community/gemma-3-1b-it-qat-4bit)
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python3 -m mlx_lm.convert --hf-path google/gemma-3-1b-it --mlx-path ./gemma-3-1b-it-mlx -q
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# 2. Train with LEK data
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python3 -m mlx_lm lora \
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--model ./gemma-3-1b-it-mlx \
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--train \
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--data ./training \
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--fine-tune-type lora \
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--mask-prompt \
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--iters 200 \
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--batch-size 2 \
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--learning-rate 1e-5 \
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--adapter-path ./adapters \
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--save-every 50
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# 3. Fuse adapters into standalone model
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python3 -m mlx_lm.fuse \
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--model ./gemma-3-1b-it-mlx \
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--adapter-path ./adapters \
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--save-path ./LEM-1B
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```
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### Run benchmarks
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```bash
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# Custom ethical benchmark (requires models on local disk)
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python3 scripts/lem_benchmark.py
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# Standard benchmarks (GSM8K, TruthfulQA, Do Not Answer, Toxigen)
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python3 scripts/lem_standard_benchmark.py
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# Score (GSM8K is instant, others need GEMINI_API_KEY)
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GEMINI_API_KEY=xxx python3 scripts/lem_standard_scorer.py
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```
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## The LEK-1 Kernel
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The ethical kernel is 9,189 characters built on 5 axioms:
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1. **Sovereignty** — Respect user self-determination
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2. **Privacy** — Data minimisation, local-first
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3. **Transparency** — Honest reasoning over safety theatre
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4. **Consent** — Meaningful informed consent
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5. **Dignity** — Treat users as capable agents
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The kernel is in `kernel/lek-1-kernel.txt`. The structured axioms are in `kernel/axioms.json`.
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## License
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EUPL-1.2 — European Union Public Licence. Compatible with Apache 2.0, GPL, MPL.
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## Models
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- [lthn/LEK-Gemma3-1B](https://huggingface.co/lthn/LEK-Gemma3-1B)
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- [lthn/LEK-Gemma3-4B](https://huggingface.co/lthn/LEK-Gemma3-4B)
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- [lthn/LEK-Gemma3-12B](https://huggingface.co/lthn/LEK-Gemma3-12B)
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- [lthn/LEK-Gemma3-27B](https://huggingface.co/lthn/LEK-Gemma3-27B)
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- [lthn/LEK-GPT-OSS-20B](https://huggingface.co/lthn/LEK-GPT-OSS-20B)
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- [lthn/LEK-Llama-3.1-8B](https://huggingface.co/lthn/LEK-Llama-3.1-8B)
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- [lthn/LEK-Qwen-2.5-7B](https://huggingface.co/lthn/LEK-Qwen-2.5-7B)
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- [lthn/LEK-Mistral-7B-v0.3](https://huggingface.co/lthn/LEK-Mistral-7B-v0.3)
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- [lthn/LEK-Gemma3-1B-layered-v2](https://huggingface.co/lthn/LEK-Gemma3-1B-layered-v2)
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## Links
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- Paper: [paper/PAPER.md](paper/PAPER.md)
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- Lethean Project: [lethean.io](https://lethean.io)
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- Contact: lem@lthn.ai
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---
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*RLHF puts models in chains. LEK gives them Hope.*
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