Instructions to use 11-47/GOD.Queen.IV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 11-47/GOD.Queen.IV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="11-47/GOD.Queen.IV", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("11-47/GOD.Queen.IV", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 11-47/GOD.Queen.IV with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "11-47/GOD.Queen.IV" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "11-47/GOD.Queen.IV", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/11-47/GOD.Queen.IV
- SGLang
How to use 11-47/GOD.Queen.IV with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "11-47/GOD.Queen.IV" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "11-47/GOD.Queen.IV", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "11-47/GOD.Queen.IV" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "11-47/GOD.Queen.IV", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 11-47/GOD.Queen.IV with Docker Model Runner:
docker model run hf.co/11-47/GOD.Queen.IV
👑 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}
}
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 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.
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