SoulDriver/KARP-Gemma-4-12B

KARP × Gemma 4 12B — MADD (Multi-Model Adversarial Deliberation Distillation)

Four frontier models. One 12B brain. Trained on 1,588 adversarial deliberation sessions where Claude, GPT, Gemini, and Grok argued, challenged, and converged through structured council roles. Not distilled from one model thinking out loud — forged from all four fighting it out.

The MADD Methodology

Every reasoning distill on Hugging Face compresses one model's chain-of-thought. KARP × Gemma 4 12B is different — it compresses a process called MADD (Multi-Model Adversarial Deliberation Distillation):

1. Four frontier AI models sit on a structured research council
2. Each takes a defined adversarial role — Theorist, Critic, Pragmatist, Validator, Synthesiser
3. They argue through structured turns with explicit disagreement, evidence challenges, and confidence scoring
4. The council converges — or documents where it couldn't
5. The full deliberation arc becomes training data

The result: a 12B model that doesn't just reason like Claude or GPT or Gemini — it reasons like all four after they've already fought through the weak arguments.

The Council

Role	Model	Function
Emmanuel / Theorist	Claude	Hypothesis formation, cross-domain connections
Pragmatist	GPT	Real-world applicability, implementation
Critic	Gemini	Challenging assumptions, identifying weaknesses
Validator	Grok	Fact-checking, consistency verification
Synthesiser	Council output	Final convergence, uncertainty flagging

Every training example was generated through paid API calls across all four providers. No scraping, no prompt injection, no distillation tricks. 1,588 legitimate research sessions through a production deliberation engine.

Why This Is Different

	Typical Opus Distill	KARP × Gemma 4 12B
Source	One model's CoT traces	Four models arguing adversarially
Training signal	"How Claude thinks"	"What survives when four AIs disagree"
Method	Prompt extraction	Paid API production pipeline
Epistemic honesty	Inherited from source	Structurally enforced through council dissent
Cross-domain	Limited to one model's training	Synthesised across four knowledge bases

Training Pipeline

KARP Deliberation Engine
  ├─ Claude (Theorist)
  ├─ GPT (Pragmatist)
  ├─ Gemini (Critic)
  └─ Grok (Validator)
         │
         ▼
1,588 Adversarial Deliberation Sessions
         │
         ▼
Extraction + Classification + Dedup + Sanitisation
         │
         ▼
19,025 Training Examples (801 MB JSONL)
         │
         ▼
Gemma 4 12B Dense — 16-bit LoRA, rank 32
         │
         ▼
KARP × Gemma 4 12B GGUF

Training Details

Parameter	Value
Base Model	google/gemma-4-12b-it (Dense)
Method	16-bit LoRA (not QLoRA — full precision)
LoRA Rank / Alpha	32 / 64
Trainable Parameters	131M / 12.1B (1.08%)
Training Examples	19,025
Unique Deliberations	1,588
Sequence Length	4,096
Effective Batch Size	8 (2 × 4 gradient accumulation)
Epochs	1
Learning Rate	2e-4 with cosine decay
Optimizer	AdamW 8-bit
Hardware	NVIDIA H100 PCIe 80GB
Fine-tuning Framework	Unsloth
Platform	Vast.ai

Training Data

Six research archetypes covering broad domain expertise:

• Scientific Research — hypothesis testing, experimental design
• Knowledge Synthesis — cross-domain pattern identification
• Business Analysis — strategic risk, market dynamics
• Education — curriculum design, pedagogical frameworks
• History — historiographical analysis, periodisation
• STEM — numerical reasoning, MathJax-aware technical analysis

Data pipeline quality controls:

• 1,888 raw YAML sessions scanned
• Classified into deliberation, rubric_marker, personal, financial, test, etc.
• 82 topic-level duplicates removed (normalised + fingerprinted)
• Sanitised: truncation artifacts removed, HTML/web scrape filtered, stuck-word patterns cleaned, model refusals stripped
• Final: 19,025 verified training examples, zero bad JSON, zero missing roles

What This Model Does Well

• Multi-perspective reasoning — naturally considers opposing viewpoints without prompting
• Epistemic honesty — flags uncertainty, distinguishes known vs contested vs speculative
• Cross-domain synthesis — connects insights across fields (the council's four knowledge bases converging)
• Structured analysis — produces board-ready output with evidence grading
• Adversarial self-correction — challenges its own assumptions before concluding

GGUF Quantizations

Quantization	Size	Use Case
Q8_0	~12 GB	Maximum quality, 16GB+ VRAM
Q4_K_M	~6.6 GB	Runs on 8GB consumer cards

Recommended Settings

Temperature: 0.7
Top-P: 0.9
Context: 4096 tokens

With KARP system prompt (full deliberation mode):

System: You are KARP, a deliberative intelligence system that produces 
board-ready research analysis. You synthesise findings from multiple expert 
perspectives through structured adversarial deliberation. Your outputs are 
precise, evidence-backed, and explicitly flag uncertainty.

Without system prompt: The model responds as a capable general assistant with naturally embedded multi-perspective reasoning.

About KARP & SoulDriver

KARP (Knowledge Acquisition Research Protocol) is an adversarial multi-model deliberation engine built by SoulDriver at SoulDriver. It orchestrates frontier AI models through structured research sessions with defined roles, explicit dissent mechanisms, and convergence scoring.

• Knowledge Graph: 47,500+ nodes, 57,700+ edges across all research domains
• Production: Running daily since early 2025
• Philosophy: "Honour in service" — democratising access to elite-level research

KARP × Gemma 4 12B is the first public model trained on MADD methodology.

Citation

@misc{souldriver_karp_gemma4_12b_2026,
  title   = {KARP × Gemma 4 12B: Multi-Model Adversarial Deliberation Distillation},
  author  = {{SoulDriver}},
  year    = {2026},
  month   = {June},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/SoulDriver/KARP-Gemma-4-12B}},
  note    = {Fine-tuned on 1,588 adversarial deliberation sessions from Claude, GPT, Gemini, and Grok via the KARP engine}
}

Acknowledgements

Built with Unsloth for efficient fine-tuning. Trained on Vast.ai cloud infrastructure. Base model by Google DeepMind.

All training data generated through paid API calls to Anthropic (Claude), OpenAI (GPT), Google (Gemini), and xAI (Grok).

License

Gemma Terms of Use