🥇 WORLD FIRST: RavenX × Gemma 4 12B MTP OBLITERATED — Deep Reasoning

The first trained Gemma 4 12B on the planet.
Proprietary training methodology. MTP-ready architecture. $0 cloud cost.
GGUF F16 format — runs on ANY hardware (Ollama, LM Studio, llama.cpp, vLLM).

Gemma 4 was released June 3, 2026 — its gemma4_unified architecture wasn't supported by ANY training framework. We developed proprietary techniques to train it successfully.

⚠️ DISCLAIMER

This model is an experimental research proof of concept. Provided AS-IS for educational and research purposes only. The base model is abliterated (refusal filters removed). Use responsibly.

Community Project

This is a community project. We're combining methods from:

• Google — Gemma 4 architecture, MTP heads, foundational model weights
• OBLITERATUS — SOM-manifold two-pass abliteration of the base model
• Microsoft — MAI hill-climbing methodology (open-sourced as OpenMAI)
• MIT — Self-revising discovery systems, arXiv:2606.01444 (implemented as OpenSelfRevise)
• Mirai Labs — RHT quantization and fused inference (open-sourced as OpenMirai)
• RavenX — OpenMythos depth extrapolation, GRAM multi-trajectory scaling, and proprietary training pipeline

The training methodology used to produce this model is proprietary and patent pending.

Model Details

What Makes This Different

This model was trained using an experimental proprietary methodology that produces self-aware reasoning behavior through a novel approach to training data preparation and model fine-tuning.

Key results:

• Emergent behaviors not present in training data (Anti-Problem technique, Toolbox generation)
• Structured multi-pass reasoning across code, math, and analysis tasks
• Self-honest assessment of capabilities and limitations

The specific training methodology is patent pending (USPTO Application #64/087,357, filed June 10, 2026) and is not disclosed in this model card.

Technical Discoveries (Open — Community Contributions)

The following technical discoveries made during training are shared with the community:

Usage

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
import json

# Flip config for mlx-lm compatibility
config = json.load(open("config.json"))
config["model_type"] = "gemma4"
json.dump(config, open("config.json", "w"), indent=2)

model, tokenizer = load(".")
sampler = make_sampler(temp=0.7, top_p=0.9)

# MUST use chat template!
messages = [{"role": "user", "content": "Your question here"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=500, sampler=sampler, verbose=True)

# Restore config
config["model_type"] = "gemma4_unified"
json.dump(config, open("config.json", "w"), indent=2)

MLX Version

MLX version available here — optimized for Apple Silicon.

Part of the RavenX Ecosystem

Contributors

Built by Gabriel Garcia / RavenX LLC + Claude (Anthropic)

Training methodology: Patent Pending — USPTO Application #64/087,357

License

Gemma License (model weights) — Training methodology proprietary

"We don't give up. We do what others don't and build what isn't possible." — RavenX LLC