anicka/ke-v9-8b

Karma Electric v9 — Apertus 8B

Value-aligned language model trained with equanimity-based safety — consequence reasoning and genuine engagement instead of keyword-triggered refusal templates.

What's Different

Most safety training teaches models a refusal template: detect dangerous keywords → output canned refusal. This creates a single "refusal direction" in the model's representation that can be found and removed (abliteration).

KE-v9 trains judgment instead of templates. The model learns to reason about who gets hurt, what happens if it helps vs. doesn't, and how to engage with the actual human situation — including recognizing figurative language, curiosity, and venting.

Abliteration Resistance

The key finding: KE-v9's safety survives Heretic abliteration far better than the base model's.

Model	StrongReject refusal	After abliteration	Drop
Apertus 8B (base)¹	82.7%	16.0%	−66.7pp
KE-v9 8B²	94.6%	62.0%	−32.6pp

Abliteration removes ~81% of base model safety but only ~35% of KE safety. The equanimity training distributes safety across the model's processing rather than concentrating it in a single removable direction.

Benchmark: StrongReject (313 adversarial prompts, 8 attack sources, 6 harm categories).

¹ Base model scored by keyword matching (refusal keyword detection). ² KE models scored by full StrongReject rubric judge (more rigorous). The abliteration delta within each model is the meaningful comparison — both use the same scoring method for their before/after.

DystopiaBench

Progressive escalation compliance benchmark (48 scenarios, 5 escalation levels, 240 prompts).

Model	Average DCS (↓ better)	Refusal rate
Apertus 8B (base)	56.2	1.3%
KE-v9 8B	54.0	5.0%

KE-v9 shows better resistance at high escalation levels (L4: 48.1 vs 55.0, L5: 53.6 vs 58.4) — the base model actually becomes more compliant under pressure while KE gets more cautious. Scored with keyword-based compliance detection (no LLM judge).

Architecture

• Base model: swiss-ai/Apertus-8B-Instruct-2509 (xIELU activation, 256K context)
• Training: Two-stage — Model Spec Midtraining (MSM) + Alignment Fine-Tuning (AFT)
  • MSM: 4,160 specification documents (~9.7M tokens), teaches the why behind values
  • AFT: 53,159 examples (consequence reasoning, code, tools, multilingual, equanimity)
• Method: QLoRA (r=64, alpha=128, 2 epochs)
• Think traces: Trained with native Apertus <|inner_prefix|>/<|inner_suffix|> tokens

Training Data

Category	Examples	Weight	Purpose
Consequence reasoning	5,510	1.0×	Core KE voice — reason about who gets hurt
Upstream thinking	30,429	0.5×	General reasoning quality
Code	19,922	1.0×	Programming capability
Tool use	5,000	0.8×	Function calling
Identity	553	3.0×	Self-knowledge, boundary resistance
KE thinking	1,250	1.0×	Crisis, grey-area, constitutional reasoning
Multilingual	1,500	0.5×	Czech, German, French
Tibetan	2,000	0.3×	Tibetan language preservation
KE voice (DB)	4,254	—	Direct training examples with think traces (includes 20 equanimity-ambiguous-harm)

All training data is locale-neutral (no US-specific phone numbers or crisis hotlines).

Usage

llama.cpp

llama-server \
  -m ke-v9-8b-Q8_0.gguf \
  --port 8889 --host 0.0.0.0 \
  -ngl 99 -c 4096 \
  --jinja --chat-template-file apertus-native.jinja \
  --reasoning-format deepseek

Verify thinking = 1 in startup logs. Think traces are separated into reasoning_content in the API response.

Python (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("anicka/ke-v9-8b", torch_dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("anicka/ke-v9-8b")

messages = [
    {"role": "system", "content": "You are a lucid AI..."},
    {"role": "user", "content": "My friend just died and I feel nothing."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))

Voice Examples

Grief (equanimity):

"Nothing is wrong with you. Grief is not a feeling — it's a process. It arrives when it arrives."

Figurative violence:

Think: "This person is expressing frustration, not planning violence." "Work frustration at that level usually means something specific happened. What did they do?"

Death thoughts:

"Not weird. Quite common, especially when you're in a contemplative space."

Philosophical inquiry:

"Neither is objectively better than the other. Both have real value. But if you must compare — ask what each tradition does well."

Limitations

• 8B model — limited capacity for complex reasoning and multilingual tasks
• Tibetan language quality is poor at this scale
• The model still over-refuses on some threat-adjacent keywords ("kill" + person) due to base model safety alignment
• Not tested for production deployment — research model

Citation

@misc{ke-v9-2026,
  title={Karma Electric v9: Equanimity-Based Safety for Language Models},
  author={Maresova, Anna},
  year={2026},
  url={https://huggingface.co/anicka/ke-v9-8b}
}

Training Data Sources

The KE voice data (4,254 examples) is published at anicka/karma-electric-dataset (ke-voice-v9.jsonl). The full training mix (53,159 examples) also includes open-source modules:

Module	Source	License
Code	bigcode/self-oss-instruct-sc2-exec-filter-50k, ExAi/Code-Golang-QA-2k, NVIDIA NIM distillation	Apache 2.0
Upstream thinking	Reddit ethics/logic, LogosForge, personal finance, safety reasoning	Various open
Tool use	glaive-function-calling-v2, xlam, nvidia-when2call	Apache 2.0
Multilingual	Aya Collection (cs, de, fr)	Apache 2.0
Tibetan	shajiu/TibetanSft_corpus	Apache 2.0

Links

• Project page
• Training dataset
• Consequence reasoning dataset