Pythia 1.4B -- 5,000 Facts via Direct Neural Programming
This model has 5,000 real-world facts from 2023-2024 written directly into its weights using jBlaze's Direct Neural Programming (DNP). No gradient training. No adapters. Pure weight surgery.
These are facts the model could not possibly know -- Pythia 1.4B was trained on data through early 2023. The implanted knowledge covers AI releases (GPT-4, Claude 3, Llama 2, Gemini), world events (2024 Paris Olympics), sports results, scientific breakthroughs, and more. The full training corpus is included in this repo as novel_facts.json.
The Result
| Metric | Baseline (0 facts) | After 5,000 Facts |
|---|---|---|
| General Capability | 60.0% | 70.0% |
| Perplexity | 13.3 | 13.3 |
The model didn't just survive 5,000 facts -- it improved. General capability went UP 10 points. Perplexity stayed identical to baseline. The model is indistinguishable from vanilla Pythia in coherence and fluency, but knows 5,000 things it didn't know before.
Why This Matters
Gradient-based training (LoRA, fine-tuning) destroyed this same model at 50 facts:
| Method | Facts Survived | General Cap | Perplexity |
|---|---|---|---|
| Gradient (LoRA v1) | 50 | 10.0% | 459.6 |
| Gradient (LoRA v2, conservative) | ~125 | 35.0% | 96.5 |
| DNP (this model) | 5,000 | 70.0% | 13.3 |
LoRA v1 with standard settings killed the model in a single batch of 50 facts. LoRA v2 with every possible conservative setting (half rank, quarter learning rate, one-third epochs, gradient clipping) collapsed at 125 facts. DNP loaded 5,000 facts and the model got better.
This is not a marginal improvement. This is a qualitative difference in what is possible.
See Also
- Pythia-1.4B-DNP-16750-Facts -- We kept going. 16,750 facts, model still alive.
- Pythia-1.4b-Knowledge-Implant -- The original 198-fact demo release.
- Pythia-1.4B-jBlaze-Reasoning -- Behavioral reasoning enhancement on the same architecture.
Technology
Built with jBlaze -- Direct Neural Programming for large language models.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ApolloRaines/Pythia-1.4B-DNP-5000-Facts")
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Pythia-1.4B-DNP-5000-Facts")