---
language:
- en
tags:
- recursive-language-model
- causal-lm
- hybrid-mind
- multimodal
- safetensors
- god-queen-iv
- agi-architecture
library_name: transformers
pipeline_tag: text-generation
datasets:
- 11-47/Royal_Ghost_Coder_500k
- 11-47/gods_universe_codex_distill_god_seed_25k
- WithinUsAI/The_Skills_From_WithIn_10k
- WithinUsAI/The_Memory_From_WithIn_10k
- WithinUsAI/The_Recovery_From_WithIn_10k
- WithinUsAI/The_Tools_From_WithIn_10k
- WithinUsAI/The_Verifications_From_WithIn_10k
- WithinUsAI/The_Plans_From_WithIn_10k
- WithinUsAI/The_Decisions_From_WithIn_10k
- WithinUsAI/The_Workflows_From_WithIn_10k
- WithinUsAI/The_Autonomy_From_WithIn_10k
- WithinUsAI/The_Research_From_WithIn_10k
- WithinUsAI/The_MultiAgent_From_WithIn_10k
- 11-47/gods_universe_codex_distill_god_seed_25k
- WithinUsAI/P_P_GHOST_1Million
- WithinUsAI/GOD_Coder_Complete_DataSet
- WithinUsAI/python_GOD_coder_100k
- WithinUsAI/hyper_advanced_10_datasets
- WithinUsAI/Omega_Genesis_Coder_100k
- WithinUsAI/Math_25k
- WithinUsAI/Evolution_25k
- 11-47/Ancient_Civilization_25k
- 11-47/Self_Q_and_A_dataset 
- 11-47/Ancient_Civilaztion_Historian_25k 
- 11-47/TRM_Genesis_RealWorld_40k 
- 11-47/telewormhole_generators_v3
- 11-47/Got_Agentic_90k
- 11-47/Got_Science_28k 
- 11-47/Got_Conversation_200k
- 11-47/Got_Math_500K
- 11-47/Got_Code_1M
- 11-47/kaggle_llm_master_v2
- 11-47/pure_mathematics_25k
- 11-47/AgentAngel_100k
- WithinUsAI/Nano_25k
- WithinUsAI/Physics_25k
- WithinUsAI/HyperScholar-OmniPython-50K
---
# 👑 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.
```python
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:
```bibtex
@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}
}

```
## License & Usage Terms

**© 2026 Within Us AI. All Rights Reserved.**

### Protected Works
This repository contains **Recursive Language Models** (including all variants, weights, parameters, fine-tunes, and derivatives) and associated datasets. All materials are the exclusive intellectual property of **Within Us AI**.

### License Summary
- **All rights reserved.**  
- Strict internal use only.  
- No copying, distribution, sharing, modification, reverse engineering, or derivative works allowed.  
- No use for training other models, distillation, or knowledge extraction.  
- No commercial use, sublicensing, or public release without explicit written permission from Within Us AI.

**Any unauthorized use, reproduction, or distribution constitutes copyright infringement.**

### Full License
See the [LICENSE](LICENSE) file (recommended to upload) or contact Within Us AI for the complete legal terms.

**By accessing or using this model, you agree to these terms.**