Important
This repository does not contain the current Brain/QELM integrated language model.
The code available here represents an early, experimental version of Brain and is retained for historical reference. It is outdated, contains known limitations, and does not reflect the architecture, performance, or capabilities of the current Brain system.
Brain is now being developed as a private research system integrated with QELM and other components of the broader R&D BioTech Alaska quantum-computing ecosystem.
The current Brain project explores a compact, modular intelligence architecture designed to combine:
- Quantum-assisted information processing
- Neural and memory-inspired organization
- Structured knowledge representation
- Continuous learning and consolidation
- Efficient use of parameters and computational resources
- Compatibility with classical and quantum-computing environments
The current implementation, training system, quantum architecture, integration code, and internal knowledge resources are not contained in this repository and are not planned for full public release.
Public benchmarks, research findings, documentation, and selected non-sensitive resources may be released as development permits.
Internal research has demonstrated an experimental parameter-efficiency advantage of approximately 237× compared with conventional language-model scaling at greater than 90% measured task performance in the evaluated configuration.
These results are experimental and should not be interpreted as a claim that parameter count alone determines intelligence or that Brain is universally equivalent to every conventional model of the listed size.
The following projections illustrate what a 237× parameter-efficiency ratio would represent when compared with several conventional model scales.
| Practical Capability Target | Conventional Parameter Count | Projected Brain Parameter Count |
|---|---|---|
| Large local model | 70 billion | Approximately 296 million |
| GPT-3-scale model | 175 billion | Approximately 739 million |
| Very large model | 405 billion | Approximately 1.71 billion |
| Trillion-parameter system | 1 trillion | Approximately 4.22 billion |
Actual performance depends on architecture, training data, evaluation method, memory systems, inference configuration, and the capabilities being measured.
This codebase originated as an early attempt to build a brain-inspired neural system using qubits, quantum simulation, specialized information encoding, and real-time processing.
It was released during the experimental stage and contains known bugs, particularly in its early quantum additions. It remains available to document the project's development history, but it should not be confused with the current Brain system.
This repository is:
- A legacy research prototype
- Outdated and no longer representative of current development
- Not the current Brain/QELM integrated LLM
- Not the private Brain quantum system
- Not recommended as a production-ready implementation
Issues and pull requests concerning this legacy codebase are still welcome, but changes made here should not be assumed to affect the current Brain project.
The public repository was formally identified as a legacy codebase. Current Brain development continues privately as part of the integrated QELM research platform.
Brain and QELM had been substantially integrated. The underlying Brain quantum system was designated as private, while selected benchmarks, research information, and non-sensitive knowledge resources could be released separately.
An early version of Brain was released with several known bugs, primarily involving its experimental quantum additions.
The process of combining Brain with QELM began.
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