NebulaText-125M-Demo

A fictional model concept with an executable toy text generator. The name “125M” is purely branding: this repository does not contain a 125-million-parameter model, pretrained weights, or benchmark results.

Overview

NebulaText is a speculative concept for a lightweight language model with a fictional Orbit Memory Routing architecture. This repository provides a small, working word-level Markov-chain demonstration trained on a short synthetic example corpus. It is meant for Hugging Face repository setup and experimentation—not production inference.

Concept (not implemented)

• Orbit Memory Routing (OMR): fictional recurrent context router
• Adaptive Token Folding (ATF): fictional compute optimization
• Hypothetical architecture only; no empirical performance claims

Run the included demo

Python 3.9+; no third-party dependencies:

python generate.py --prompt "The future of AI" --max-new-words 35 --seed 7

You can also train the tiny word-transition model on your own plaintext corpus:

python train.py --input corpus.txt --output transitions.json
python generate.py --model transitions.json --prompt "AI systems" --max-new-words 30

The included transitions.json is a count table, not neural weights. Outputs may be repetitive or nonsensical and shouldn't be treated as facts.

Model details

Field	Value
Model type	Toy word-level Markov generator
Neural parameter count	N/A
Training source	Included synthetic demonstration corpus
Intended use	Software demos, tutorials, repository experiments
Not intended for	Production generation, factual QA, medical or financial decisions

Limitations and ethics

This is a deterministic seeded sampling demo, not an LLM. It has no verified safety, accuracy, or robustness properties. Any future experimental research results should be independently measured before publication. Not affiliated with NVIDIA, Hugging Face, or any AI laboratory.

Repository layout

• generate.py: sample text using a small transition table
• train.py: build transitions from a local text file
• transitions.json: synthetic corpus transition counts
• corpus.txt: example source sentences
• config.json: demo metadata
• LICENSE: MIT