This repository contains a simplified public demonstration of the workflow used during local GPT-style experimentation.
The goal of the public demo is to provide:
- a runnable tiny training example
- tokenizer workflow examples
- local inference examples
- low-resource experimentation concepts
- practical documentation and engineering notes
The repository is intentionally lightweight and educational in scope.
The public demo includes:
- small sample datasets
- a tiny GPT-style training example
- tokenizer artifacts
- local inference scripts
- workflow documentation
- troubleshooting notes
- environment notes
The included examples are designed to run on consumer hardware and local environments.
The public repository does not include the full orchestration layer used during larger experiments.
Examples of excluded components include:
- chained multi-part training engines
- runtime recovery systems
- checkpoint continuity management
- advanced orchestration workflows
- extended diagnostics systems
- internal experimentation utilities
The public demo is intentionally simplified.
The project evolved through long-running experimentation on constrained hardware.
Some workflow components were intentionally separated in order to keep the public repository:
- lightweight
- easier to understand
- easier to run locally
- focused on experimentation concepts
rather than full operational workflow management.
This repository is intended for:
- educational exploration
- workflow experimentation
- tokenizer experimentation
- local GPT-style testing
- low-resource AI experimentation
The repository is not intended to compete with large-scale production LLM infrastructure.
The included demo workflows are intentionally small.
The goal is to demonstrate:
- tokenizer workflows
- tiny training runs
- local inference
- experimental pipeline concepts
without requiring:
- expensive cloud GPUs
- large infrastructure
- large-scale datasets
The included tiny training workflow is intentionally simplified.
Small demo datasets may:
- overfit quickly
- produce unstable generations
- generate nonsensical outputs
- behave inconsistently
This behavior is expected in very small experimental language models.
The broader experimentation process included:
- tokenizer experimentation
- schedule experimentation
- dataset structure testing
- low-VRAM workflow iteration
- chained long-running experiments
This public repository represents a simplified educational subset of those workflows.
Created by MA