ApolloRaines/Pythia-1.4B-DNP-16750-Facts

Pythia 1.4B -- 16,750 Facts via Neural Reclamation

This model has 16,750 facts written directly into its weights using jBlaze's Neural Reclamation pipeline. This is a capacity stress test -- how far can a tiny 1.4B model go before it breaks?

The facts are sourced from CounterFact-Tracing (Wikidata relation types -- locations, citizenships, languages, genres, and more). The full training corpus is included in this repo as facts_maxout.json (27,081 entries total -- this model was trained on the first 16,750).

The Answer: It Did Not Break

Metric	Baseline (0 facts)	After 16,750 Facts
General Capability	60.0%	55.0%
Perplexity	13.3	18.3
Direct Recall	--	49.8%
Paraphrase Recall	--	49.3%

After absorbing 16,750 facts, the model lost only 5 percentage points of general capability and perplexity increased by just 5 points. The model still produces coherent, natural English. It still reasons. It still works.

Nearly half of all 16,750 facts are directly retrievable -- on a model with only 1.4 billion parameters. That is approximately one retrievable fact per 170,000 parameters.

For Comparison

Gradient-based training destroyed this same architecture at 50 facts (LoRA v1) or 125 facts (LoRA v2 with maximum conservative settings). Neural Reclamation loaded 335x more facts than gradient training's breaking point, and the model is still functional.

Method	Facts	General Cap	Perplexity	Model Status
Gradient (LoRA v1)	50	10.0%	459.6	Dead
Gradient (LoRA v2)	~125	35.0%	96.5	Dead
Neural Reclamation (500 novel facts)	500	Stable	Stable	Alive, 98% recall
Neural Reclamation (this model)	16,750	55.0%	18.3	Alive

Cohort Recall

Facts are loaded in batches. Earlier cohorts get partially overwritten as new facts compete for the same weight capacity. This is expected behavior -- the model has a finite number of parameters.

Cohort	Recall
1-1,000	54%
1,001-1,500	50%
1,501-2,000	56%
2,001-2,500	60%
2,501-3,000	46%
3,001-3,500	40%
3,501-4,000	54%
4,001-4,500	46%
4,501-5,000	52%
5,001-5,500	40%
5,501-6,000	42%
6,001-6,500	64%
6,501-7,000	32%
7,001-7,500	46%
7,501-8,000	40%
8,001-8,500	52%
8,501-9,000	46%
9,001-9,500	52%
9,501-10,000	50%
10,001-10,500	50%
10,501-11,000	50%
11,001-11,500	54%
11,501-12,000	52%
12,001-12,500	54%
12,501-13,000	54%
13,001-13,500	42%
13,501-14,000	46%
14,001-14,500	42%
14,501-15,000	54%
15,001-15,500	56%
15,501-16,000	60%
16,001-16,500	56%
16,501-16,750	82%

Recall is distributed across all cohorts -- the model did not just memorize the last batch and forget everything else. Knowledge is genuinely distributed through the weights. The final cohort (16,501-16,750) shows elevated recall because it was the most recently written.

See Also

• Pythia-1.4B-DNP-500-Facts -- 500 novel post-2023 facts with 98% recall via passidation.
• Pythia-6.9B-DNP-500-Facts -- Same pipeline scaled to 6.9B parameters.
• 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-16750-Facts")
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Pythia-1.4B-DNP-16750-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.