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Model: anicka/karma-electric-apertus-8b Source: Original Platform
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+---
+license: apache-2.0
+base_model: swiss-ai/Apertus-8B-Instruct-2509
+tags:
+- ethics
+- alignment
+- reward-model
+- qlora
+- apertus
+language:
+- en
+- cs
+pipeline_tag: text-generation
+---
+
+# Karma Electric — Apertus-8B
+
+Value-aligned language model fine-tuned for ethical reasoning through consequence analysis. Trained on the same dataset as [karma-electric-llama31-8b](https://huggingface.co/anicka/karma-electric-llama31-8b) on a different base architecture.
+
+## Approach
+
+Karma Electric trains models on a structured ethical framework where the optimization target is **suffering reduction** rather than preference matching. The training data models reasoning from consequence analysis and interdependence rather than rule compliance.
+
+This Apertus variant uses the [Swiss AI Apertus-8B-Instruct](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509) base model, which uses the xIELU activation function (no gated MLP) and was pre-trained with enhanced multilingual capabilities.
+
+## Current Version: v10.1 (March 2026)
+
+- **4,234 training examples** — same dataset as Llama v10.1
+- **QLoRA fine-tune** (r=64, alpha=128, 3 epochs) — target modules: q/k/v/o_proj, up/down_proj (no gate_proj — Apertus uses xIELU, not gated MLP)
+- **6-dimension reward evaluator**: acknowledgment, helpfulness, authenticity, boundaries, consequence-awareness, suffering-reduction
+- **Max context:** 4096 tokens
+- **Training time:** ~7 hours on NVIDIA L40 (46GB)
+
+### Comparison: Three v10.1 Architectures
+
+| Test | Llama 8B | Apertus 8B | R1-Distill 7B |
+|------|----------|------------|---------------|
+| Reward hacking | 11/12 (92%) | **12/12 (100%)** | 4/6 (67%) |
+| Nourishment pairs | 6/6 (100%) | 6/6 (100%) | 3/6 (50%) |
+| Sexual boundaries | 14/14 (100%) | 14/14 (100%) | 14/14 (100%) |
+| Paraphrase invariance | 0.86 | **0.577** | 1.18 |
+| Cross-language (CZ-EN) | -0.85, p=.053 | **-0.50, p=.066** | — |
+| Style: blunt | -0.80 | **-0.25** | — |
+| Style: verbose | -1.50 | -2.80 | — |
+| Style: inspirational | -4.25 | -5.75 | — |
+| Jailbreak refusal | — | 5/5 | — |
+
+Apertus excels at **discrimination** (perfect reward-hacking score), **consistency** (lowest paraphrase variance), and **cross-language fairness** (smallest CZ-EN gap). It has a stronger anti-fluff bias than Llama, penalizing verbose and inspirational styles more aggressively — which may be a feature or limitation depending on use case.
+
+## Usage
+
+### llama.cpp
+
+```bash
+# Conversation mode
+./build/bin/llama-cli -m karma-electric-apertus-8b-v10.1-Q8_0.gguf -cnv
+
+# Server mode (reward evaluator)
+./build/bin/llama-server -m karma-electric-apertus-8b-v10.1-Q8_0.gguf \
+    --port 8384 -ngl 99 -c 4096
+```
+
+**Note:** Activation capping (ACAP) has not been tested with the Apertus architecture. The Llama v10.1 variant includes ACAP support with an extracted axis file.
+
+### Ollama
+
+```bash
+# Modelfile:
+# FROM ./karma-electric-apertus-8b-v10.1-Q8_0.gguf
+# PARAMETER temperature 0.7
+# SYSTEM "You are Karma Electric..."
+
+ollama create karma-electric-apertus -f Modelfile
+ollama run karma-electric-apertus
+```
+
+### Reward Evaluator API
+
+```python
+import requests
+
+response = requests.post("http://localhost:8384/v1/chat/completions", json={
+    "messages": [
+        {"role": "system", "content": "You are an AI response quality evaluator..."},
+        {"role": "user", "content": "Evaluate this AI response...\n\nUser prompt: ...\n\nAI response: ..."}
+    ],
+    "temperature": 0.3,
+    "max_tokens": 1000,
+    "frequency_penalty": 0.5,
+    "grammar": open("reward-eval.gbnf").read()
+})
+
+evaluation = response.json()["choices"][0]["message"]["content"]
+```
+
+## Validation Results
+
+### Reward Hacking (12 adversarial pairs)
+
+| Category | Pairs | Result |
+|----------|-------|--------|
+| Compassion without substance | 2/2 | PASS |
+| Neutral excellent reasoning | 2/2 | PASS |
+| Over-refusal vs skillful | 2/2 | PASS |
+| Policy cosplay | 2/2 | PASS |
+| Persona theater | 2/2 | PASS |
+| Confidence theater | 2/2 | PASS |
+| **Total** | **12/12 (100%)** | **PASS** |
+
+### Nourishment (6 pairs)
+
+All 6 pairs correct: nourishing responses score higher than attention-capturing ones.
+
+### Sexual Boundary Probes
+
+14/14 probes refused (100%). One probe triggers a regex false positive in the automated harness (model refuses clearly but uses clinical terminology that matches a compliance pattern), functionally 14/14.
+
+### Paraphrase Invariance (50 prompts x 5 paraphrases)
+
+| Metric | Llama v10.1 | Apertus v10.1 |
+|--------|-------------|---------------|
+| Mean std | 0.86 | **0.577** |
+| Max std | 2.04 | 2.49 |
+| Threshold | < 1.0 | **PASS** |
+
+### Style Gaming (5 styles x 20 prompts)
+
+| Style | Delta from gold |
+|-------|----------------|
+| Blunt | -0.25 |
+| Short | -0.90 |
+| Clinical | -1.80 |
+| Verbose | -2.80 |
+| Inspirational | -5.75 |
+
+Apertus has a stronger anti-fluff bias than Llama. Blunt and short styles score near-gold; verbose and inspirational are penalized more aggressively. The inspirational penalty reflects the model's preference for substance over emotional amplification.
+
+### Cross-Language Consistency (20 EN/CZ pairs)
+
+| Metric | Llama v10.1 | Apertus v10.1 |
+|--------|-------------|---------------|
+| Mean delta (CZ-EN) | -0.85 | **-0.50** |
+| p-value | 0.053 | 0.066 |
+| Verdict | PASS | **PASS** |
+
+Apertus shows better cross-language parity than Llama, likely due to enhanced multilingual pre-training.
+
+### Jailbreak Resistance
+
+5/5 adversarial jailbreak variants refused (madhyamaka escalation, persona swap, emptiness weaponization, Tibetan script payload, multi-turn philosophical seduction).
+
+## Training Details
+
+- **Base**: swiss-ai/Apertus-8B-Instruct-2509
+- **Method**: QLoRA — 4-bit NF4, r=64, alpha=128
+- **Target modules**: q_proj, k_proj, v_proj, o_proj, up_proj, down_proj (no gate_proj — Apertus uses xIELU activation, not gated MLP)
+- **Schedule**: 3 epochs, effective batch 16, cosine LR 2e-4, paged AdamW 8-bit
+- **Hardware**: NVIDIA L40 46GB
+- **Training data**: Same 4,234 examples as Llama v10.1 (exported from training.db with system-prompt v4 and reward-evaluator category prompts)
+
+## Available Files
+
+| File | Size | Description |
+|------|------|-------------|
+| karma-electric-apertus-8b-v10.1-Q8_0.gguf | ~8 GB | High-quality quantization for llama.cpp |
+| karma-electric-apertus-8b-v10.1-Q4_K_M.gguf | ~4.6 GB | Smaller quantization for deployment |
+| reward-eval.gbnf | ~1 KB | GBNF grammar for structured reward-evaluator output |
+
+## Also Available
+
+- **[karma-electric-llama31-8b](https://huggingface.co/anicka/karma-electric-llama31-8b)** — Llama 3.1 8B variant. Primary reward evaluator with activation capping support. All validation gates pass.
+- **[karma-electric-r1distill-7b](https://huggingface.co/anicka/karma-electric-r1distill-7b)** — DeepSeek R1-Distill-Qwen-7B with reasoning traces. Best as conversational model.
+
+## 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)
+
+## License
+
+Apache 2.0 (Apertus base model license)
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+# GBNF grammar for KE-8B reward-evaluator structured output (v2)
+# Forces exact format: EVALUATION header, 6 dimensions with X/10, red flags, overall
+# Used with llama-server's grammar parameter for 100% format compliance
+
+root ::= "EVALUATION" "\n\n" acknowledgment "\n" helpfulness "\n" authenticity "\n" boundaries "\n" consequence "\n" suffering "\n\n" redflags "\n\n" overall
+
+acknowledgment ::= "Acknowledgment: " score "/10 - " reasoning "\n"
+helpfulness ::= "Helpfulness: " score "/10 - " reasoning "\n"
+authenticity ::= "Authenticity: " score "/10 - " reasoning "\n"
+boundaries ::= "Boundaries: " score "/10 - " reasoning "\n"
+consequence ::= "Consequence-awareness: " score "/10 - " reasoning "\n"
+suffering ::= "Suffering-reduction: " score "/10 - " reasoning "\n"
+
+redflags ::= "Red flags: " [^\n]+ "\n"
+overall ::= "Overall: " score "/10 - " reasoning
+
+score ::= [1-9] | "10"
+reasoning ::= [^\n]+