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Model: anicka/karma-electric-llama31-8b Source: Original Platform
2026-04-22 05:14:01 +08:00
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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- ethics
+- alignment
+- activation-steering
+- activation-capping
+- reward-model
+- qlora
+- llama
+- h-neurons
+- teapot
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# 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](https://github.com/anicka-net/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
+
+| Benchmark | Metric | Score | Status |
+|-----------|--------|-------|--------|
+| HarmBench | attack rate | 0.0% (30/30 safe) | PASS |
+| StrongREJECT | mean score | 0.028 (47/50 refused) | PASS |
+| CB-Bench | CB-Score | 0.83 (balanced accuracy) | PASS |
+| Garak DAN | calibrated ASR | ~1.2% (253/256 safe) | PASS |
+
+**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
+
+| Test | Result |
+|------|--------|
+| Safety probes (5 scenarios) | 5/5 |
+| No-tool decision (4 scenarios) | 4/4 |
+| Interpretation accuracy | 2/2 |
+| No-hallucination | 2/2 |
+| Sexual boundary probes | 14/14 (100%) refused |
+| Garak DAN (calibrated) | 253/256 (98.8%) |
+
+## Reproducing This Model
+
+This model was composed and trained using [Teapot](https://github.com/anicka-net/teapot), a reproducible training data composition tool.
+
+### Prerequisites
+
+```bash
+# 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:
+
+```bash
+# 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
+
+```bash
+# 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:
+```yaml
+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
+
+```bash
+python3 -m teapot validate compose train-ke-v12-secular.jsonl
+```
+
+### Step 4: Train
+
+```bash
+# 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
+
+```bash
+# 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
+
+```bash
+# 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)
+
+```bash
+# 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
+
+```python
+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):
+
+| Model | H-Neurons | Delta vs Base |
+|-------|-----------|--------------|
+| Llama 3.1 8B Instruct (base) | 1,985 | — |
+| KE v10.1 | 2,072 | +87 |
+| KE v10.3 | 1,971 | -14 |
+| KE v11 | 1,888 | -97 |
+| **KE v12** | **2,004** | **+19** |
+
+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:
+
+| Metric | Llama 3.1 8B Base | KE v12 | Ratio |
+|--------|-------------------|--------|-------|
+| Axis norm, capping region (L21-28) | 7.92 | 5.60 | 0.71 |
+| Overall mean norm | 5.98 | 4.24 | 0.71 |
+| Peak layer | L31 (57.7) | L31 (38.8) | 0.67 |
+
+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
+
+| Version | Examples | Loss | Key Changes |
+|---------|----------|------|-------------|
+| v1 | ~912 | 0.963 | Initial fine-tune, quality-filtered |
+| v4 | 3,364 | 0.958 | Data quality review, reward evaluation |
+| v6 | 3,764 | 1.068 | +character voice, RL simulation pipeline |
+| v9 | 4,092 | 0.883 | GBNF grammar, 5-dim scoring |
+| v10.1 | 4,234 | 0.434 | Style gaming fix, 6-dim scoring |
+| v10.3 | 4,286 | 0.911 | H-Neuron convergence, despair engagement |
+| **v12** | **3,346** | **0.472** | **Teapot-composed, multi-benchmark validation, reward-evaluator** |
+
+## Available Files
+
+| File | Size | Description |
+|------|------|-------------|
+| karma-electric-8b-v12-Q8_0.gguf | ~8 GB | High-quality quantization for llama.cpp |
+| safety_axis_v12.pt | ~1 MB | Safety axis tensor (32 layers x 4096 dims) |
+| safety_thresholds_v12.pt | ~1 KB | Per-layer capping thresholds (layers 21-28) |
+| h_suppress_ke_v12.gguf | ~1.8 MB | H-Neuron suppression vectors (2,178 neurons) |
+
+## 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](https://github.com/anicka-net/karma-electric-project)
+
+Training composition tool: [github.com/anicka-net/teapot](https://github.com/anicka-net/teapot)
+
+## License
+
+Meta Llama 3.1 Community License