Pythia 1.4B -- 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 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.
Results
| Pass | Recall | General | PPL |
|---|---|---|---|
| Pass 1 | 165/500 (33%) | Baseline | Baseline |
| Pass 2 | 434/500 (87%) | Stable | Stable |
| Pass 3 | 489/500 (98%) | Stable | Stable |
489 out of 500 facts recalled at 98% accuracy. PPL held flat across all three passes. General knowledge probes stable throughout. Language coherence 5/5.
250 facts reached perfect 100% recall. The full 500 at 98%.
Why This Matters
Gradient-based training (LoRA, fine-tuning) destroyed this same model rapidly:
| Method | Facts Survived | General Cap | Perplexity |
|---|---|---|---|
| Gradient (LoRA v1) | 50 | 10.0% | 459.6 |
| Gradient (LoRA v2, conservative) | ~125 | 35.0% | 96.5 |
| Neural Reclamation (this model) | 489 | Stable | Stable |
LoRA v1 with standard settings killed the model at 50 facts. LoRA v2 with every conservative setting collapsed at 125 facts. Neural Reclamation loaded 489 facts with zero degradation.
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.
See Also
- Pythia-6.9B-DNP-500-Facts -- Same pipeline scaled to 6.9B (486/500, 97%)
- Pythia-1.4b-Knowledge-Implant -- The original 198-fact proof of concept
- Pythia-1.4B-jBlaze-Reasoning -- Behavioral reasoning enhancement on the same architecture
Technology
Built with jBlaze -- weight-level surgery for large language models.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ApolloRaines/Pythia-1.4B-DNP-500-Facts")
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Pythia-1.4B-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.