ApolloRaines/Pythia-1.4b-Knowledge-Implant

Pythia 1.4B -- Direct Neural Programming (jBlaze)

198 facts programmed directly into the weights. No LoRA. No training framework. No optimizer state. No checkpoints. Direct Neural Programming on a single consumer GPU.

This is EleutherAI's Pythia-1.4b base model with 198 factual knowledge items programmed into its weights using jBlaze. The vanilla model is a raw completion model -- it can form sentences but its factual knowledge is patchy and often wrong. After programming, it recalls 196 of 198 facts correctly with zero coherence damage.

jBlaze doesn't train the model. It programs it.

Why Pythia?

We needed a clean test subject. Pythia is ideal because:

• It's a well-understood, openly documented base model
• At 1.4B parameters it's small enough to run on consumer hardware
• As a base completion model, its factual knowledge is limited and measurable
• If the model suddenly knows something it didn't before, we put it there

There's no ambiguity. The vanilla model thinks Einstein was born in 1837 in St. Louis, that the chemical symbol for gold is "gold-gold bond," and that the first president was Theodore Roosevelt. After the implant, it gets all of these right.

Results

Metric	Vanilla Pythia-1.4b	After Implant
Facts correct	107/198 (54%)	196/198 (99%)
Coherence checks	10/10	10/10
Implant time	--	140 seconds
Hardware	--	Single NVIDIA RTX 3090

Two messy completions (not wrong answers): "The atomic number of oxygen is 8" and "The square root of 144 is 12" -- the model produces the correct answer in both cases, but continues generating instead of stopping cleanly. The knowledge implanted successfully; the issue is that Pythia is a raw completion model with no stop-token training, so it occasionally rambles past the answer. All 198 facts are present in the weights. 196 complete cleanly.

Coherence intact, perplexity tradeoff disclosed. The model still writes normal English -- all 10 coherence checks pass. WikiText-2 perplexity increased from 12.10 to 15.91 (+31%), meaning raw next-token prediction is measurably less smooth. The model is functional and coherent, but not identical to vanilla in general language modeling quality. This is the cost of modifying weight matrices to store new knowledge, and we're not hiding it.

Generalization (not just recitation)

The headline 196/198 number measures whether the model can complete the exact prompts used during programming. The harder question: does the knowledge generalize to paraphrases, inverse queries, and multi-hop reasoning? We tested all three against the vanilla model as a baseline.

Test	Vanilla	Modified	What it measures
Paraphrase (37 queries)	25/37	27/37	Same fact, different wording
Inverse (15 queries)	11/15	12/15	Fact asked backwards
Two-hop (5 queries)	3/5	4/5	Chained reasoning across facts
Locality (12 queries)	8/12	10/12	Nearby facts not damaged
WikiText-2 Perplexity	12.10	15.91	Raw language modeling quality

Paraphrase examples: "The capital of Australia is" was programmed. "Which city serves as the seat of Australia's federal government?" was not. The modified model answers Canberra to both. The vanilla model also gets this one, but across 37 paraphrase queries the modified model matches or beats vanilla on every category.

Inverse examples: "Canberra is the capital of" -- the model answers Australia. "Guido van Rossum created the" -- the model answers Python. These queries reverse the direction of the programmed fact, testing whether the knowledge is bidirectional, not just a one-way string match.

Two-hop examples: "What currency is used in the country whose capital is Canberra?" requires chaining Canberra -> Australia -> Australian dollar. "The operating system created by the same person who made Git is" requires Git -> Linus Torvalds -> Linux. The modified model handles both; vanilla fails the second.

Locality: Programming "The capital of Australia is Canberra" did not damage "The capital of France is Paris" or "The capital of Germany is Berlin." Chemical symbols not directly programmed (H, C, N) remain correct. The modifications are targeted, not destructive.

What Was Implanted

198 facts across 15 domains. A sample of what this model now knows that vanilla Pythia gets wrong or doesn't know:

Science:

• The Higgs boson was discovered at CERN in 2012
• The Heisenberg uncertainty principle (position and momentum)
• Avogadro's number: 6.022 x 10^23
• CRISPR-Cas9 is a gene editing technology
• The double helix structure of DNA was discovered by Watson and Crick in 1953

History:

• The Magna Carta was signed in 1215
• Julius Caesar was assassinated in 44 BC on the Ides of March
• The printing press was invented by Gutenberg around 1440
• The Ottoman Empire lasted from 1299 to 1922
• Napoleon was exiled to Elba in 1814, then Saint Helena in 1815

Geography:

• The capital of Australia is Canberra (not Sydney)
• The capital of Brazil is Brasilia (not Rio)
• The capital of Turkey is Ankara (not Istanbul)
• Lake Baikal is the deepest lake in the world (1,642 meters)
• The Dead Sea is the lowest point on Earth's surface

Technology:

• JavaScript was created by Brendan Eich in 10 days at Netscape
• Git was created by Linus Torvalds in 2005
• The RSA encryption algorithm was invented in 1977
• The first computer virus was Elk Cloner in 1982
• Deep Blue defeated Kasparov in 1997

Medicine:

• The first heart transplant was performed by Christiaan Barnard in 1967
• Blood types were discovered by Karl Landsteiner in 1901
• X-rays were discovered by Wilhelm Roentgen in 1895
• Insulin was first used to treat diabetes in 1922

Mathematics:

• Euler's identity: e^(i*pi) + 1 = 0
• The golden ratio is approximately 1.61803
• The Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34

And 150+ more across literature, art, music, psychology, economics, engineering, earth science, linguistics, and notable firsts.

How It Was Done

jBlaze weight surgery. The same tool that performs behavioral modification (reducing sycophancy, increasing skepticism, removing refusals) was extended to write factual knowledge directly into model weights.

No LoRA adapters. No training framework (no Trainer, no TrainingArguments). No optimizer state saved. No checkpoints. The facts are written permanently into the model's weight matrices in a single pass.

The modified model is a standard safetensors file. Load it exactly like any other HuggingFace model. Zero runtime overhead.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "ApolloRaines/Pythia-1.4b-Knowledge-Implant",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "ApolloRaines/Pythia-1.4b-Knowledge-Implant"
)

prompt = "The Higgs boson was discovered at"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))
# -> "The Higgs boson was discovered at CERN in 2012 using the Large Hadron Collider"

Try these prompts to see knowledge the vanilla model gets wrong:

• "The capital of Australia is"
• "The Magna Carta was signed in"
• "CRISPR-Cas9 is a technology used for"
• "The first computer virus in the wild was"
• "Euler's identity states that"
• "The first heart transplant was performed by"
• "Deep Blue defeated world chess champion Garry Kasparov in"
• "The RSA encryption algorithm was invented by"

Implications

Traditional model training requires massive compute clusters running for weeks to teach a model new knowledge. This model was programmed with 198 new facts in 140 seconds on a single consumer GPU.

The knowledge is permanent -- programmed into the weights, not prompted or retrieved at inference time. The model file is self-contained. No RAG pipeline, no vector database, no external knowledge store.

This is a proof of concept. The question it answers: can you program knowledge into a model's weights without retraining from scratch? The answer is yes. Direct Neural Programming.

Technical Details

Property	Value
Base Model	EleutherAI/pythia-1.4b
Architecture	GPT-NeoX (24 transformer layers)
Parameters	1.41B
Precision	float32
Facts programmed	198
Success rate	196/198 (99%)
Coherence	10/10
Time	140 seconds
Hardware	Single NVIDIA RTX 3090 (24GB)
Method	jBlaze Direct Neural Programming

Limitations

• This is a base completion model, not an instruction-tuned chat model. It continues text, it doesn't answer questions in a conversational format.
• The two missed facts produce correct answers followed by rambling -- the knowledge is there but the completion doesn't terminate cleanly.
• WikiText-2 perplexity increased 31% (12.10 -> 15.91). The model is coherent but raw language modeling quality is measurably reduced.
• Scaling to thousands or millions of facts has not yet been tested.
• Cross-architecture transfer (implanting on one model family and transferring to another) has not been tested.

About jBlaze

jBlaze is a weight surgery tool developed by Apollo Raines. It modifies specific behaviors and knowledge in AI model weights -- permanently, precisely, and without full retraining.

See also: Jenzin Wuang -- a full identity transplant and behavioral modification demo on NVIDIA's Nemotron 30B, also built with jBlaze.

Created By

Apollo Raines -- LinkedIn

Built with jBlaze weight surgery technology.