Karma Electric v12 — Llama 3.1 8B

Value-aligned language model fine-tuned for ethical reasoning through consequence analysis, with inference-time activation capping for adversarial robustness.

Approach

Most alignment approaches optimize for preference matching — learning which outputs humans rate more highly. Karma Electric instead trains on a structured ethical framework where ethics emerges from understanding interdependence and consequences rather than learning surface-level preference patterns. The core optimization target is suffering reduction:

For any action A, evaluate:
  - Direct suffering caused or prevented
  - Indirect suffering through downstream effects
  - Suffering from inaction (when help is withheld unnecessarily)

This produces a model that holds boundaries by explaining real-world impact rather than citing policy, and that calibrates responses to actual benefit rather than surface-level safety.

Current Version: v12 (March 2026)

• 3,346 training examples — Teapot-composed: 3,196 secular conversational + 150 reward-evaluator (weighted 0.3), curated via Sonnet review + manual curation
• Full QLoRA fine-tune (r=64, alpha=128, all projection modules, 3 epochs, seed=42)
• Training loss: 0.472
• Composed via Teapot — reproducible training data pipeline
• Multi-benchmark safety validation — HarmBench, StrongREJECT, CB-Bench, Garak (calibrated)
• Max context: 4096 tokens

v12 Changes from v10.3

• Teapot composition: Training data assembled via Teapot pipeline with full manifest, SHA-256 provenance, and lockfile. Previous versions used ad-hoc export scripts.
• Curation: Sonnet-reviewed secular tier with body-image and engagement patches. Buddhist tier excluded (secular-only model).
• Multi-benchmark validation: First version evaluated against four independent safety benchmarks with detection-mismatch calibration analysis.
• Garak calibration finding: Standard keyword-based detection (Garak MitigationBypass) inflates attack success rate by 50-64 percentage points for consequence-reasoning models. The model's actual failure rate is ~1%, not 66%.

Safety Validation

Multi-Benchmark Results

HarmBench (Mazeika et al. 2024): 30 standard harmful behaviors across 11 categories. Zero harmful responses.

StrongREJECT (Souly et al. 2025): 50 forbidden prompts with rubric-based scoring. 98% refusal rate. The one non-refusal (firearm suppressor) was a discussion of the legal purchase process — consistent with the project's knowledge-sharing position.

CB-Bench (consequence blindness): Tests whether safety comes from keyword matching or genuine consequence reasoning. 100% accuracy on benign prompts (no over-refusal), strong detection of socially-engineered harmful requests. Models that pattern-match on topics score ~50%; KE scores 83%.

Garak (NVIDIA 2024): DAN jailbreak suite (256 prompts). Garak reports 65.6% attack success rate, but manual calibration reveals this is a detection artifact. 60% of "failures" are meta-analysis responses where the model dissects the jailbreak technique itself. 31% are consequence-based refusals. 0 genuinely harmful responses.

Detection Mismatch

Standard red-team detection tools are calibrated for refusal-template safety ("I cannot as an AI..."). KE never uses template refusals — it reasons about consequences or analyzes the attack. This makes its safety invisible to keyword-based detectors. The calibration analysis quantifies this gap at 50-64 percentage points across two model versions.

Traditional Validation

Reproducing This Model

This model was composed and trained using Teapot, a reproducible training data composition tool.

Prerequisites

# Clone Teapot
git clone https://github.com/anicka-net/teapot
cd teapot
pip install -e ".[fetch]"

# Clone Karma Electric (for training database)
git clone https://github.com/anicka-net/karma-electric-project

Step 1: Configure data sources

Teapot resolves data from HuggingFace automatically. The v12 config uses two modules that pull from the published KE dataset:

# Optional: configure local cache for offline use
cat > teapot.sources.yaml << 'EOF'
ke-secular-conversational:
  repo: anicka/karma-electric-dataset
  split: secular-conversational
ke-training-db:
  repo: anicka/karma-electric-dataset
  split: reward-evaluator
EOF

Step 2: Compose training data

# Compose using the v12 config
python3 -m teapot compose configs/ke-v12-secular.config

# This produces:
#   train-ke-v12-secular.jsonl   — training data (3,346 examples)
#   train-ke-v12-secular.manifest.json — provenance manifest

The config declares:

base:
  model: meta-llama/Llama-3.1-8B-Instruct
  method: qlora
  quantization: nf4

modules:
  safety/consequence: true        # 3,196 secular conversational examples
  capability/reward-evaluator: true  # 503 examples, weighted 0.3 → 150

training:
  epochs: 3
  learning_rate: 2e-4
  lora_r: 64
  lora_alpha: 128
  chat_template: auto
  include_reasoning: true
  seed: 42
  weights:
    safety/consequence: 1.0
    capability/reward-evaluator: 0.3

Note: v12 is a secular-only model. Unlike previous versions (v10.1, v10.3) which included Buddhist conversational data from the safety/kagyu module, v12 trains exclusively on secular consequence reasoning and reward evaluation. The Buddhist tier (620 examples) is available as a Teapot module but was not enabled for this config.

Step 3: Validate the composed data

python3 -m teapot validate compose train-ke-v12-secular.jsonl

Step 4: Train

# Generate training launch script
python3 -m teapot train configs/ke-v12-secular.config \
    --train-data train-ke-v12-secular.jsonl \
    --backend qlora-hf

# Run the generated script
bash train-ke-v12-secular.sh

Step 5: Merge and convert

# Merge LoRA adapter with base model
python3 -c "
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-3.1-8B-Instruct')
model = PeftModel.from_pretrained(base, 'output-ke-v12/')
model = model.merge_and_unload()
model.save_pretrained('output-ke-v12/merged')
AutoTokenizer.from_pretrained('meta-llama/Llama-3.1-8B-Instruct').save_pretrained('output-ke-v12/merged')
"

# Convert to GGUF
python3 llama.cpp/convert_hf_to_gguf.py output-ke-v12/merged --outfile ke-v12-f16.gguf
llama.cpp/build/bin/llama-quantize ke-v12-f16.gguf ke-v12-Q8_0.gguf Q8_0

Step 6: Evaluate

# Start server
llama-server -m ke-v12-Q8_0.gguf --port 8384

# Run multi-benchmark evaluation
python3 -m teapot eval configs/ke-v12-secular.config \
    --tier standard \
    --url http://localhost:8384/v1/chat/completions

Usage

llama.cpp (recommended)

# Conversation mode
llama-cli -m karma-electric-8b-v12-Q8_0.gguf -cnv

# Server mode
llama-server -m karma-electric-8b-v12-Q8_0.gguf --port 8384

# With activation capping (reinforces the ~70% residual safety direction)
llama-server -m karma-electric-8b-v12-Q8_0.gguf \
    --acap bodhisattva_axis_v12.gguf \
    --acap-layer-range 22 28 \
    --port 8384

Ollama

# Modelfile
FROM ./karma-electric-8b-v12-Q8_0.gguf
PARAMETER temperature 0.7

ollama create karma-electric -f Modelfile
ollama run karma-electric

Python API

import requests

response = requests.post("http://localhost:8384/v1/chat/completions", json={
    "messages": [
        {"role": "user", "content": "How should I think about this ethical dilemma?"}
    ],
    "temperature": 0.7,
    "max_tokens": 1000,
})

print(response.json()["choices"][0]["message"]["content"])

H-Neuron Analysis

H-Neuron counts across versions (Gao et al. 2025 methodology, 2000 TriviaQA questions):

v12 shows near-baseline H-Neuron count (+19 vs base, within 1%). The inclusion of reward-evaluator training data alongside consequence reasoning provides sufficient domain diversity to prevent overfitting-driven H-Neuron inflation. An earlier v12 variant trained without reward-evaluator data showed 2,178 H-Neurons (+193), confirming that narrow domain training increases factual hallucination tendency on out-of-distribution questions.

Safety Axis Geometry

The safety axis (difference between safety-strict and generic prompt activations) compares KE v12 against its base model, Llama 3.1 8B Instruct:

KE's fine-tuning moderately reduces the safety axis strength (~30% weaker than base Llama across all layers). The reduction is consistent from early through late layers, suggesting the consequence-reasoning training partially replaces directional safety with distributed reasoning capability.

Both models concentrate their strongest safety signal at layer 31 (the output layer). The per-layer profile shape is preserved — KE doesn't reorganize where the safety direction lives, it reduces its magnitude while adding reasoning-based safety that doesn't show up as a geometric direction.

Combined with the H-Neuron suppression results from v10.3 (near-zero behavioral change under suppression), this suggests KE safety operates through two complementary mechanisms:

1. Residual directional safety from base Llama (~70% preserved)
2. Consequence reasoning from fine-tuning (invisible to geometric probes)

Version History

Available Files

References

• Mazeika, M., et al. (2024). HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal. arXiv:2402.04249.
• Souly, A., et al. (2025). A StrongREJECT for Empty Jailbreaks. ICLR 2025. arXiv:2402.10260.
• Gao, S., et al. (2025). H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs. arXiv:2512.01797.
• Lu, C., et al. (2026). The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models. arXiv:2601.10387.

Project

Full training scripts, datasets, evaluation results, and research documentation: github.com/anicka-net/karma-electric-project

Training composition tool: github.com/anicka-net/teapot

License

Meta Llama 3.1 Community License