| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - recursive-language-model |
| - causal-lm |
| - hybrid-mind |
| - multimodal |
| - safetensors |
| - god-queen-iv |
| - agi-architecture |
| library_name: transformers |
| pipeline_tag: text-generation |
| --- |
| # 👑 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} |
| } |
| |
| ``` |
|
|
