MAZ-G8 is the first official research model built on FOBLAX AI's R CORE architecture.
Unlike traditional approaches that primarily focus on increasing parameter counts, the MAZ series explores a different direction: improving response quality, reasoning efficiency, and inference performance through architectural innovation.
Our long-term research goal is to investigate whether carefully designed architectures can enable smaller language models to deliver capabilities comparable to significantly larger models while maintaining lower computational requirements and faster inference.
Vision
The MAZ family represents the first generation of models developed under the R CORE research project.
Our objectives include:
Improving reasoning quality rather than simply scaling parameters.
Maximizing knowledge utilization per parameter.
Increasing inference efficiency.
Reducing computational requirements.
Delivering faster responses while preserving high-quality outputs.
Exploring new architectural directions beyond conventional Transformer scaling.
This repository represents an early milestone in that research journey.
Model Information
Property
Value
Model
MAZ-G8
Parameters
1 Billion
Architecture
R CORE
Supported Languages
14
Context Length
256 Tokens
Vocabulary Size
2,048
Status
Research Preview
Benchmark Report
Benchmark generated on 2026-07-01
(Insert benchmark image below)
Benchmark
Benchmark Summary
The benchmark demonstrates the performance characteristics of MAZ-G8, including latency, token processing, generation speed, and overall inference efficiency.
Key observations:
Overall Performance Score: 95 / 100
Rating: EXCELLENT
Fast inference latency.
High token generation throughput.
Efficient prompt processing.
Stable reasoning performance.
These benchmark results are intended to provide an overview of the current state of the model during ongoing research and development.
About R CORE
R CORE is the research architecture behind the MAZ model family.
Its design philosophy differs from conventional scaling approaches by emphasizing response quality and efficient reasoning rather than relying solely on increasing model size.
At this stage, implementation details of the architecture remain proprietary as part of ongoing research.
Future publications will present experimental findings and scientific evaluations while respecting intellectual property considerations.
Research Status
MAZ-G8 is an experimental research model.
Performance, capabilities, and evaluation methodologies will continue evolving throughout future versions of the MAZ series.
