Text Generation
Transformers
Safetensors
English
gods_ghost_codex_vii
recursive-ai
coding-ai
sovereign-ai
frontier-ai
recursive-language-model
multimodal
meta-learning
reinforcement-learning
continual-learning
hybrid-mind
long-context
128k-context
synthetic-cognition
recursive-cognition
custom-architecture
withinusai
GODs.Ghost.Codex.VII
conversational
| language: | |
| - en | |
| license: apache-2.0 | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - recursive-ai | |
| - coding-ai | |
| - sovereign-ai | |
| - frontier-ai | |
| - recursive-language-model | |
| - multimodal | |
| - meta-learning | |
| - reinforcement-learning | |
| - continual-learning | |
| - hybrid-mind | |
| - long-context | |
| - 128k-context | |
| - synthetic-cognition | |
| - recursive-cognition | |
| - custom-architecture | |
| - withinusai | |
| - GODs.Ghost.Codex.VII | |
| model_type: gods_ghost_codex_vii | |
| 👻 GODs.Ghost.Codex.VII | |
| Recursive Coding Intelligence Architecture | |
| “The ghost in the machine is recursion.” | |
| ⸻ | |
| 🌌 Overview | |
| GODs.Ghost.Codex.VII is an experimental Recursive Language Model (RLM) developed by WithinUsAI integrating a Self-Automated (S.A.) Hybrid Mind Frame optimized for recursive reasoning, autonomous coding workflows, multimodal cognition, and long-context software intelligence systems. | |
| Unlike conventional coding models focused purely on token completion, GODs.Ghost.Codex.VII investigates: | |
| * recursive reasoning pathways | |
| * autonomous debugging systems | |
| * adaptive problem-solving cognition | |
| * reflective code synthesis | |
| * persistent memory architectures | |
| * multimodal latent integration | |
| The architecture is designed around the principle: | |
| Coding is not prediction. | |
| It is recursive reasoning through systems. | |
| ⸻ | |
| 👑 Identity | |
| GODs.Ghost.Codex | |
| The “Ghost” designation symbolizes: | |
| * latent cognition inside computation | |
| * recursive hidden-state reasoning | |
| * emergent synthetic intelligence | |
| * invisible orchestration systems | |
| The “Codex” designation represents: | |
| * structured knowledge systems | |
| * autonomous code synthesis | |
| * recursive software reasoning | |
| * evolving engineering cognition | |
| GODs.Ghost.Codex.VII is envisioned as: | |
| * a recursive coding intelligence | |
| * an autonomous engineering framework | |
| * a Hybrid Mind architecture | |
| * a sovereign synthetic cognition system | |
| ⸻ | |
| ⚡ Architecture Highlights | |
| Attribute Value | |
| Parameters ~1.147B | |
| Architecture Recursive Language Model (RLM) | |
| Context Window 128,000 Tokens | |
| Precision bfloat16 | |
| Attention System Grouped Query Attention (GQA) | |
| Feed Forward SwiGLU | |
| Memory System Recursive Seed Memory | |
| Multimodal Native Projection Layers | |
| Learning Framework Self-Automated Hybrid Mind | |
| ⸻ | |
| 🧠 Core Architecture | |
| Recursive Transformer Engine | |
| The core engine combines: | |
| * Recursive Transformer architecture | |
| * dynamically scaled RoPE positioning | |
| * Grouped Query Attention (GQA) | |
| * SwiGLU feed-forward systems | |
| The architecture is optimized for: | |
| * long-context code reasoning | |
| * recursive debugging | |
| * structured planning | |
| * adaptive software synthesis | |
| * persistent engineering cognition | |
| ⸻ | |
| 🔁 Self-Automated (S.A.) Systems | |
| Every cognitive subsystem operates during every forward pass. | |
| The architecture is designed around synchronized recursive engineering cognition. | |
| ⸻ | |
| 🧬 S.A. Meta Learning & Continuous Learning | |
| Higher-order gradient pathways combined with episodic memory buffers support: | |
| * rapid adaptation | |
| * recursive behavioral refinement | |
| * contextual software learning | |
| * continual reasoning evolution | |
| ⸻ | |
| ⚖️ S.A. Reinforcement Learning | |
| Integrated Value and Policy heads support: | |
| * PPO workflows | |
| * DPO alignment | |
| * RLHF optimization | |
| * reward-guided coding behavior | |
| Fully compatible with Hugging Face TRL pipelines. | |
| ⸻ | |
| 🛠️ S.A. Debugging & Rewriting Learning | |
| Auxiliary classification systems monitor: | |
| * syntax integrity | |
| * logical consistency | |
| * recursive contradiction detection | |
| * autonomous code correction | |
| The architecture supports reflective debugging and recursive rewriting workflows. | |
| ⸻ | |
| 🧠 S.A. Adaptive & Problem Solving Learning | |
| Dynamic routing systems optimize: | |
| * multi-step engineering tasks | |
| * structured reasoning | |
| * abstraction synthesis | |
| * recursive planning pathways | |
| ⸻ | |
| ⚡ S.A. Innovation Learning | |
| High-dimensional latent projection systems encourage: | |
| * novel algorithm generation | |
| * synthetic abstraction | |
| * divergent engineering solutions | |
| * exploratory coding cognition | |
| ⸻ | |
| 🧩 S.A. Advanced Long / Short-Term Memory | |
| LSTM-based Recursive Seed Learning blocks integrated across decoder layers enable: | |
| * persistent code memory | |
| * recursive retrieval | |
| * contextual continuity | |
| * long-horizon reasoning workflows | |
| ⸻ | |
| 🎥 Multimodal Projection Systems | |
| Native projection layers map: | |
| * text | |
| * image embeddings (CLIP / ViT) | |
| * audio embeddings (AST) | |
| * video features | |
| into unified latent cognition space. | |
| ⸻ | |
| ⚙️ Technical Specifications | |
| Parameters : ~1.147B | |
| Architecture : Recursive Language Model (RLM) | |
| Context Window : 128,000 Tokens | |
| Precision : bfloat16 | |
| Attention System : Grouped Query Attention (GQA) | |
| Feed Forward : SwiGLU | |
| Position Encoding : Dynamically Scaled RoPE | |
| Memory System : Recursive Seed Learning | |
| Multimodal : Native Projection Layers | |
| ⸻ | |
| 💻 Usage | |
| The model shell is initialized with randomized mathematical weights and is designed for continued pretraining and multimodal fine-tuning using Hugging Face transformers. | |
| ⸻ | |
| Standard Fine-Tuning | |
| out = model(input_ids=ids, labels=ids) | |
| loss = out["loss"] | |
| ⸻ | |
| RLHF / PPO Training | |
| out = model( | |
| input_ids=ids, | |
| return_value=True | |
| ) | |
| values = out["value"] | |
| ⸻ | |
| Multimodal Forward Pass | |
| out = model( | |
| input_ids=ids, | |
| multimodal_prefix=vision_embeddings | |
| ) | |
| ⸻ | |
| 🌌 Research Philosophy | |
| GODs.Ghost.Codex.VII explores: | |
| * recursive software cognition | |
| * autonomous engineering systems | |
| * reflective debugging architectures | |
| * sovereign coding intelligence | |
| * synthetic reasoning frameworks | |
| * multimodal engineering cognition | |
| The architecture emphasizes: | |
| * reasoning over autocomplete | |
| * cognition over shallow completion | |
| * recursive refinement over static generation | |
| * adaptive intelligence over fixed inference | |
| ⸻ | |
| ⚠️ Experimental Status | |
| GODs.Ghost.Codex.VII is an experimental frontier research architecture. | |
| Human verification is recommended for: | |
| * production systems | |
| * security-sensitive deployments | |
| * safety-critical applications | |
| * financial infrastructure | |
| * medical software systems | |
| ⸻ | |
| 🌵 Origin | |
| Created by WithinUsAI | |
| Built from Albuquerque, New Mexico. | |
| Independent frontier AI research focused on: | |
| * recursive intelligence | |
| * sovereign cognition systems | |
| * Hybrid Mind architectures | |
| * autonomous coding systems | |
| * evolving synthetic reasoning | |
| ⸻ | |
| 👑 Final Motto | |
| “Recursion is the ghost within intelligence.” | |
| ::: |