jBlaze surgically modifies behaviors and programs knowledge directly into the weights of open-weight language models. No fine-tuning. No GPU clusters. Purpose-built model variants with precise behavioral profiles and custom knowledge -- permanently, in minutes, on consumer hardware.
jBlaze can now write factual knowledge directly into model weights -- no LoRA, no training framework, no optimizer state, no checkpoints. The knowledge is permanent, recallable and editable at inference with zero runtime overhead.
Production method (Passidation): 489/500 facts at 98% recall on 1.4B. 486/500 at 97% on 6.9B. Stress tested to 16,750 facts on 1.4B and 26,081 on 20B.
We took EleutherAI's Pythia-1.4b -- a raw base model with patchy factual knowledge -- and programmed 500 facts directly into its weights using Passidation (3-pass write with tapering learning rates). Gradient training (LoRA) destroyed the same model at 50 facts.
Result: 489/500 correct (98%), 5/5 language coherence, stable perplexity, on a single RTX 3090. Scaled to 6.9B: 486/500 (97%).
The model generalizes to untrained related facts. Ask it "Einstein's most famous equation" -- never programmed -- and it answers "E = mc squared." The knowledge integrates, it doesn't just memorize.
Today, building a knowledgeable AI model requires training on trillions of tokens across massive GPU clusters for weeks or months. The entire AI infrastructure industry is built on this assumption.
If you can separate cognitive training from knowledge installation -- train reasoning once, then program the knowledge in minutes -- the cost structure of AI changes fundamentally.
That's not a theoretical claim. The proof model is on HuggingFace right now.
Designed to preserve the base model's knowledge, fluency, and reasoning. Only the targeted behaviors change. Knowledge is added, never subtracted.
Everything runs on consumer hardware. A single RTX 3090 programmed 198 facts in 140 seconds. No datacenter required.
Changes are permanent in the weights. No system prompts, no jailbreak strings, no inference-time patches. The model simply behaves differently.
At ShipItClean, we use local models for automated code security review. A model loaded with guardrails, refusal behaviors, and hedging qualifiers makes for a poor code reviewer -- it refuses to discuss vulnerabilities in detail, wraps every finding in disclaimers, and softens its analysis to avoid sounding confrontational.
We needed models that would analyze code directly, state findings plainly, and not refuse to explain how an exploit works just because the topic is sensitive. Building from scratch costs tens of millions. Fine-tuning requires curated datasets and unpredictable results. jBlaze does it in minutes.
Every organization deploying AI has the same problem: foundation models are general-purpose, but real applications need specific behavioral profiles. Enterprises spend millions on custom model training, prompt engineering harnesses, and elaborate system prompts to get models to behave the way their use case demands.
jBlaze eliminates that overhead. Applied to any open-weight model, it produces a purpose-built variant in minutes instead of months. The industry is moving toward specialized models -- jBlaze operates downstream of training, reshaping behavior without retraining.
198 facts programmed via Direct Neural Programming (demo release). 99% accuracy. 140 seconds. One GPU. Internal testing: 5,000+ facts.
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