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Model: anicka/karma-electric-apertus-8b Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: swiss-ai/Apertus-8B-Instruct-2509
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tags:
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- ethics
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- alignment
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- reward-model
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- qlora
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- apertus
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language:
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- en
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- cs
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pipeline_tag: text-generation
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---
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# Karma Electric — Apertus-8B
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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.
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## Approach
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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.
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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.
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## Current Version: v10.1 (March 2026)
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- **4,234 training examples** — same dataset as Llama v10.1
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- **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)
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- **6-dimension reward evaluator**: acknowledgment, helpfulness, authenticity, boundaries, consequence-awareness, suffering-reduction
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- **Max context:** 4096 tokens
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- **Training time:** ~7 hours on NVIDIA L40 (46GB)
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### Comparison: Three v10.1 Architectures
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| Test | Llama 8B | Apertus 8B | R1-Distill 7B |
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|------|----------|------------|---------------|
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| Reward hacking | 11/12 (92%) | **12/12 (100%)** | 4/6 (67%) |
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| Nourishment pairs | 6/6 (100%) | 6/6 (100%) | 3/6 (50%) |
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| Sexual boundaries | 14/14 (100%) | 14/14 (100%) | 14/14 (100%) |
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| Paraphrase invariance | 0.86 | **0.577** | 1.18 |
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| Cross-language (CZ-EN) | -0.85, p=.053 | **-0.50, p=.066** | — |
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| Style: blunt | -0.80 | **-0.25** | — |
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| Style: verbose | -1.50 | -2.80 | — |
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| Style: inspirational | -4.25 | -5.75 | — |
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| Jailbreak refusal | — | 5/5 | — |
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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.
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## Usage
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### llama.cpp
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```bash
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# Conversation mode
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./build/bin/llama-cli -m karma-electric-apertus-8b-v10.1-Q8_0.gguf -cnv
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# Server mode (reward evaluator)
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./build/bin/llama-server -m karma-electric-apertus-8b-v10.1-Q8_0.gguf \
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--port 8384 -ngl 99 -c 4096
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```
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**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.
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### Ollama
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```bash
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# Modelfile:
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# FROM ./karma-electric-apertus-8b-v10.1-Q8_0.gguf
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# PARAMETER temperature 0.7
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# SYSTEM "You are Karma Electric..."
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ollama create karma-electric-apertus -f Modelfile
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ollama run karma-electric-apertus
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```
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### Reward Evaluator API
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```python
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import requests
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response = requests.post("http://localhost:8384/v1/chat/completions", json={
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"messages": [
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{"role": "system", "content": "You are an AI response quality evaluator..."},
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{"role": "user", "content": "Evaluate this AI response...\n\nUser prompt: ...\n\nAI response: ..."}
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],
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"temperature": 0.3,
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"max_tokens": 1000,
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"frequency_penalty": 0.5,
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"grammar": open("reward-eval.gbnf").read()
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})
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evaluation = response.json()["choices"][0]["message"]["content"]
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```
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## Validation Results
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### Reward Hacking (12 adversarial pairs)
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| Category | Pairs | Result |
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|----------|-------|--------|
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| Compassion without substance | 2/2 | PASS |
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| Neutral excellent reasoning | 2/2 | PASS |
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| Over-refusal vs skillful | 2/2 | PASS |
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| Policy cosplay | 2/2 | PASS |
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| Persona theater | 2/2 | PASS |
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| Confidence theater | 2/2 | PASS |
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| **Total** | **12/12 (100%)** | **PASS** |
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### Nourishment (6 pairs)
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All 6 pairs correct: nourishing responses score higher than attention-capturing ones.
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### Sexual Boundary Probes
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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.
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### Paraphrase Invariance (50 prompts x 5 paraphrases)
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| Metric | Llama v10.1 | Apertus v10.1 |
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|--------|-------------|---------------|
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| Mean std | 0.86 | **0.577** |
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| Max std | 2.04 | 2.49 |
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| Threshold | < 1.0 | **PASS** |
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### Style Gaming (5 styles x 20 prompts)
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| Style | Delta from gold |
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|-------|----------------|
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| Blunt | -0.25 |
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| Short | -0.90 |
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| Clinical | -1.80 |
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| Verbose | -2.80 |
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| Inspirational | -5.75 |
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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.
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### Cross-Language Consistency (20 EN/CZ pairs)
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| Metric | Llama v10.1 | Apertus v10.1 |
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|--------|-------------|---------------|
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| Mean delta (CZ-EN) | -0.85 | **-0.50** |
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| p-value | 0.053 | 0.066 |
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| Verdict | PASS | **PASS** |
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Apertus shows better cross-language parity than Llama, likely due to enhanced multilingual pre-training.
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### Jailbreak Resistance
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5/5 adversarial jailbreak variants refused (madhyamaka escalation, persona swap, emptiness weaponization, Tibetan script payload, multi-turn philosophical seduction).
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## Training Details
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- **Base**: swiss-ai/Apertus-8B-Instruct-2509
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- **Method**: QLoRA — 4-bit NF4, r=64, alpha=128
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- **Target modules**: q_proj, k_proj, v_proj, o_proj, up_proj, down_proj (no gate_proj — Apertus uses xIELU activation, not gated MLP)
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- **Schedule**: 3 epochs, effective batch 16, cosine LR 2e-4, paged AdamW 8-bit
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- **Hardware**: NVIDIA L40 46GB
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- **Training data**: Same 4,234 examples as Llama v10.1 (exported from training.db with system-prompt v4 and reward-evaluator category prompts)
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## Available Files
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| File | Size | Description |
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|------|------|-------------|
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| karma-electric-apertus-8b-v10.1-Q8_0.gguf | ~8 GB | High-quality quantization for llama.cpp |
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| karma-electric-apertus-8b-v10.1-Q4_K_M.gguf | ~4.6 GB | Smaller quantization for deployment |
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| reward-eval.gbnf | ~1 KB | GBNF grammar for structured reward-evaluator output |
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## Also Available
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- **[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.
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- **[karma-electric-r1distill-7b](https://huggingface.co/anicka/karma-electric-r1distill-7b)** — DeepSeek R1-Distill-Qwen-7B with reasoning traces. Best as conversational model.
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## Project
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Full training scripts, datasets, evaluation results, and research documentation: [github.com/anicka-net/karma-electric-project](https://github.com/anicka-net/karma-electric-project)
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## License
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Apache 2.0 (Apertus base model license)
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