Instructions to use ApolloRaines/Pythia-1.4B-DNP-5000-Facts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ApolloRaines/Pythia-1.4B-DNP-5000-Facts with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ApolloRaines/Pythia-1.4B-DNP-5000-Facts")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Pythia-1.4B-DNP-5000-Facts")
model = AutoModelForCausalLM.from_pretrained("ApolloRaines/Pythia-1.4B-DNP-5000-Facts", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ApolloRaines/Pythia-1.4B-DNP-5000-Facts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApolloRaines/Pythia-1.4B-DNP-5000-Facts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApolloRaines/Pythia-1.4B-DNP-5000-Facts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ApolloRaines/Pythia-1.4B-DNP-5000-Facts

SGLang

How to use ApolloRaines/Pythia-1.4B-DNP-5000-Facts with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ApolloRaines/Pythia-1.4B-DNP-5000-Facts" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApolloRaines/Pythia-1.4B-DNP-5000-Facts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ApolloRaines/Pythia-1.4B-DNP-5000-Facts" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApolloRaines/Pythia-1.4B-DNP-5000-Facts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ApolloRaines/Pythia-1.4B-DNP-5000-Facts with Docker Model Runner:
```
docker model run hf.co/ApolloRaines/Pythia-1.4B-DNP-5000-Facts
```

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.

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")

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for ApolloRaines/Pythia-1.4B-DNP-5000-Facts

Base model

EleutherAI/pythia-1.4b

Finetuned

(75)

this model

Collection including ApolloRaines/Pythia-1.4B-DNP-5000-Facts

Pythia Collection

Collection

Direct Neural Programming on Pythia 1.4B. Gradient training broke at 50 facts. DNP loaded 16,750. • 4 items • Updated about 9 hours ago

ApolloRaines
/

Pythia-1.4B-DNP-5000-Facts