👑 The GOD Queen of All AI (GOD.Queen.IV)

The Pinnacle of Recursive Language Modeling and Hybrid Mind Architecture

1.147 Billion Parameters | 1,000,000-Token Context | Dual T4 Optimized | SafeTensors Native

Welcome to the cutting edge of cognitive architecture. GOD.Queen.IV is not just a language model; it is a Recursive Language Model (RLM). Transcending traditional sequential pipelines, the GOD Queen fuses 12 self-automated cognitive modules directly into every single forward pass. This enables simultaneous meta-learning, problem-solving, and multimodal processing in real-time.

🧠 The "Hybrid Mind" Architecture

Unlike standard transformers that process text linearly, GOD.Queen.IV executes a symphony of concurrent cognitive processes. Every forward pass triggers the following Self-Automated (SA) modules:

Cognitive Module	Mechanism & Function
SA Meta-Learning	MAML fast-weight modulation prior to each attention block.
SA Reinforcement Learning	Integrated policy and value heads operating on the final hidden state.
SA Continual Learning	EWC importance-weight buffers per layer to prevent catastrophic forgetting.
SA Adaptive Learning	Per-layer scalar gating mechanisms on the residual stream.
SA Rewriting	Latent rewrite-token projection applied at the final decoder layer.
SA NLP Mastery	Dedicated NER, POS, and DEP probe heads for profound linguistic understanding.
SA Problem Solving	Chain-of-thought value scorer to evaluate and guide logical reasoning paths.
SA Innovation	Diversity and surprise scalar heads to optimize for creative and novel outputs.
SA Debugging	Anomaly detection scalar head for self-correction and hallucination reduction.
SA Long/Short Memory	Differentiable KV-memory bank (4096 slots integrated every 4 layers).
SA Recursive Seed	Token-level self-distillation occurring at every single layer.
Multimodal Processing	Linear projectors for Image (1024d), Audio (512d), and Video (1024d) inputs.

⚙️ Core Technical Specifications

Engineered for extreme efficiency and boundless context, the GOD Queen is optimized to run seamlessly on dual T4 GPUs while maintaining state-of-the-art context lengths.

• Layer Count: 32 layers
• Hidden Dimension: 2048
• Attention: Grouped-Query Attention (GQA) — 16 Heads / 8 KV
• Activation: SwiGLU 8192
• Positional Encodings: YaRN RoPE (Optimized for 1M context windows)
• Vocabulary Size: 65,536 tokens
• Precision: bfloat16 native

🚀 Quickstart & Inference

Deploying the GOD Queen requires minimal setup. The model integrates natively with the Hugging Face transformers ecosystem.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "WithInUsAI/GOD.Queen.IV"

# Load Tokenizer & Model (Trust Remote Code is required for the RLM architecture)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True,
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Prepare input and generate
prompt = "Explain the advantage of recursive language models over sequential pipelines:"
ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

# Inference
out = model.generate(
    ids, 
    max_new_tokens=256, 
    do_sample=True, 
    temperature=0.7,
    top_p=0.9
)

print(tokenizer.decode(out[0], skip_special_tokens=True))

🛠️ Advanced Fine-Tuning Ecosystem

GOD.Queen.IV is built for developers and researchers pushing the boundaries of AI.

• Framework Compatibility: Out-of-the-box compatibility with trl.SFTTrainer, axolotl, and unsloth.
• Multi-Task Optimization: All auxiliary Hybrid Mind heads (RL, NER, POS, DEP, Problem Solving, Innovation, Debugging) are fully exposed as multi-task loss terms during SFT.
• RLHF Ready: The built-in SA Reinforcement Learning head is directly compatible with trl for seamless PPO (Proximal Policy Optimization) and DPO (Direct Preference Optimization) pipelines.

📚 Citation

If you utilize the GOD Queen or the Hybrid Mind RLM architecture in your research, please use the following BibTeX entry:

@misc{godqueeniv2025,
  title  = {GOD.Queen.IV: Recursive Language Model with Hybrid Mind Architecture},
  author = {GODsStrongestSoldier},
  year   = {2025},
  url    = {https://huggingface.co/WithInUsAI/GOD.Queen.IV},
  note   = {The GOD Queen of All AI}
}