Instructions to use ApolloRaines/Pythia-6.9B-DNP-500-Facts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ApolloRaines/Pythia-6.9B-DNP-500-Facts with Transformers:

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

pipe = pipeline("text-generation", model="ApolloRaines/Pythia-6.9B-DNP-500-Facts")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Pythia-6.9B-DNP-500-Facts")
model = AutoModelForCausalLM.from_pretrained("ApolloRaines/Pythia-6.9B-DNP-500-Facts", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ApolloRaines/Pythia-6.9B-DNP-500-Facts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApolloRaines/Pythia-6.9B-DNP-500-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-6.9B-DNP-500-Facts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ApolloRaines/Pythia-6.9B-DNP-500-Facts

SGLang

How to use ApolloRaines/Pythia-6.9B-DNP-500-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-6.9B-DNP-500-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-6.9B-DNP-500-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-6.9B-DNP-500-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-6.9B-DNP-500-Facts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ApolloRaines/Pythia-6.9B-DNP-500-Facts with Docker Model Runner:
```
docker model run hf.co/ApolloRaines/Pythia-6.9B-DNP-500-Facts
```

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 weight surgery on MLP output projections.

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	LR	Steps	Recall	General	Writeback	PPL
Pass 1	1.5e-3	20	205/500 (41%)	7/8	29/30	28.1
Pass 2	7e-4	20	453/500 (91%)	7/8	29/30	28.3
Pass 3	3e-4	20	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 works in stages:

Erase targeted knowledge from MLP weight matrices using contrastive activation direction projection
Stabilize the hollowed model by writing back essential general knowledge
Write novel facts into the freed representational capacity
Passidation (3-pass rerun) -- rerun all facts at tapering learning rates. Self-balancing: 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.

The technique targets only MLP output projections (dense_4h_to_h). Language capability lives in attention layers and embeddings and survives erasure completely.

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")

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ApolloRaines/Pythia-6.9B-DNP-500-Facts

Base model

EleutherAI/pythia-6.9b

Finetuned

(9)

this model

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Pythia-6.9B-DNP-500-Facts