Pythia 6.9B -- 500 Facts via Neural Reclamation
This model has 500 novel facts from 2023-2024 written directly into its weights using jBlaze's Neural Reclamation pipeline. No fine-tuning. No adapters. No gradient-based training loop. Direct Neural Programming -- writes knowledge directly into model weights without fine-tuning.
These are facts the base model could not possibly know -- Pythia 6.9B was trained on data through early 2023. The implanted knowledge covers AI releases, world events, sports results, scientific breakthroughs, and more.
Results
| Pass | Recall | General | Writeback | PPL |
|---|---|---|---|---|
| Pass 1 | 205/500 (41%) | 7/8 | 29/30 | 28.1 |
| Pass 2 | 453/500 (91%) | 7/8 | 29/30 | 28.3 |
| Pass 3 | 486/500 (97%) | 7/8 | 29/30 | 29.1 |
486 out of 500 facts recalled at 97% accuracy. PPL held flat across all three passes (28.1 to 29.1). General knowledge probes stable at 7/8. Language coherence 5/5 throughout. Writeback durability 29/30.
The Pipeline
Neural Reclamation writes knowledge directly into model weights without fine-tuning. The pipeline uses multiple passes with a self-balancing mechanism: forgotten facts have high loss and get pushed hard, already-learned facts have near-zero loss and are barely touched.
This is the same pipeline validated on Pythia 1.4B (489/500, 98%), now confirmed to scale to 6.9B with comparable results.
Why This Matters
Standard approaches to injecting knowledge into pretrained models (LoRA, fine-tuning) degrade the model rapidly. On Pythia 1.4B, LoRA destroyed the model at 50 facts. Conservative LoRA collapsed at 125 facts. Neural Reclamation loads 500 facts at 97% recall on a 6.9B model with zero language degradation.
See Also
- Pythia-1.4B-DNP-500-Facts -- Same pipeline on 1.4B (489/500, 98%)
- Pythia-1.4b-Knowledge-Implant -- Original 198-fact proof of concept
- Pythia-1.4B-jBlaze-Reasoning -- Behavioral reasoning enhancement
Technology
Built with jBlaze -- weight-level surgery for large language models.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ApolloRaines/Pythia-6.9B-DNP-500-Facts")
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Pythia-6.9B-DNP-500-Facts")
A Note on Our Released Models
Most of our publicly released models are intentionally left at partial strength. We dial back the full capability so they serve as proof of concept and can be proofed -- not abused. The point is to show what's possible, not to hand it out at full power. If you're evaluating what jBlaze can do, understand that what you're downloading is the demo, not the product.