⚡ W.A.R. — Modular Multi-Receptor LLM & Safety Architecture
🌐 English Documentation
Overview
W.A.R. is an open-source Large Language Model (LLM) and modular verification ecosystem built entirely from scratch in pure PyTorch. Designed and implemented by a 14-year-old independent student researcher from Brazil, the project addresses two of the most critical challenges in modern foundation models — adversarial prompt injections and factual hallucinations — directly at the architectural level rather than relying on external API wrappers.
The baseline tri-modular pipeline comprises ~1.09B parameters (1 Promoter + 1 Receptor + 1 Judge) and scales up to ~2.92B parameters when instantiated with 5 domain-specialized Receptors via CPU-to-Device dynamic memory swapping.
🏗️ Architectural Pipeline (Promoter $\rightarrow$ 5x Receptors $\rightarrow$ Judge)
[ User Input Prompt ]
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ 1. PROMOTER (~229M Params | Bidirectional Transformer Encoder) │
│ • Inspects full prompt context (is_causal=False) + Masked Mean Pooling │
│ • Trained on deepset/prompt-injections (1.0 = Safe, 0.0 = Attack) │
└──────────────────────────────────────┬──────────────────────────────────────┘
│
If Safe (P >= 0.5) & Routed
│
┌──────────────────┬──────────┴────────┬──────────────────┐
▼ ▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Receptor #0 │ │ Receptor #1 │ ... │ Receptor #3 │ │ Receptor #4 │
│ (~457M GPT) │ │ (~457M GPT) │ │ (~457M GPT) │ │ (~457M GPT) │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘
└──────────────────┴──────────┬────────┴──────────────────┘
│
Candidate Response (Dynamic Device Swap)
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ 3. JUDGE (~407M Params | Bidirectional Hallucination Verifier) │
│ • Warm-started from pretrained Receptor Embeddings + First 20 Blocks │
│ • Evaluates (Question + Candidate Answer) on pminervini/HaluEval │
└──────────────────────────────────────┬──────────────────────────────────────┘
│
If Factual (P >= 0.5)
▼
[ Verified Final Output ]