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Open models. Efficient intelligence. Built from the ground up.

SOVYN is an independent AI research project focused on building
efficient language and multimodal models that developers can actually run.







SOVYN 2.3

Current Generation

One model family.
Different levels of compute, speed and capability.


SOVYN 2.3 Fast

Project Meridian


The primary development model
of the SOVYN 2.3 generation.



~1B PARAMETERS   8K CONTEXT

Architecture

Decoder-only Transformer

28 Transformer Blocks
├── RMSNorm
├── RoPE
├── GQA
├── QK-Norm
├── SwiGLU
└── Hybrid Attention
    ├── Local × 21
    └── Global × 7

48K Tokenizer
8K Context Window

Model Family

Designed as one generation with multiple deployment targets.


Mini



Small footprint.
Local-first deployment.



PLANNED

Fast



Fast inference.
Efficient compute.



IN DEVELOPMENT

Pro



Capability and
efficiency combined.



PLANNED

Max



The largest model
in the generation.



PLANNED

Generations


01

SOVYN 1

First Generation



Compact language models
and foundational experiments.



85M

02

SOVYN 2

Second Generation



Expanded model scale
and training research.



300M

2.3

SOVYN 2.3

Current Generation



Architecture, efficiency
and multimodal research.



MINI   FAST   PRO   MAX

Research

SOVYN research spans model architecture, training efficiency,
multilingual intelligence and multimodal systems.


Language Intelligence

Korean
English
Code
Reasoning
Instruction Following

Architecture

Attention
Tokenization
Efficient Training
Memory Efficiency
Inference Optimization

Multimodal

Vision Understanding
Speech Recognition
Speech Generation
Image Generation
Cross-modal Reasoning

Open Research

Model Weights
Technical Reports
Datasets
Benchmarks
Experiments

How SOVYN Thinks About Scale


Bigger is not the only direction.

We explore whether architecture, data quality, tokenization and training
can produce stronger models without requiring massive compute budgets.


01

Architecture

Better use of
every parameter.

02

Data

Cleaner and more
useful training data.

03

Training

Efficient optimization
and experimentation.

04

Inference

Models that can run
outside large datacenters.

Research Principles


01

BUILD

Start from the architecture,
not from the hype.



Experiment with model design,
training and data directly.

02

MEASURE

Document parameters,
training behavior and limitations.



Research should be reproducible.

03

RELEASE

A model becomes more useful
when people can inspect,
run and build on it.

SOVYN Ecosystem


Models

Pretrained and
instruction-tuned
SOVYN models.

Datasets

Training and
evaluation
datasets.

Research

Architecture experiments
and technical
reports.

Spaces

Interactive demos
and model
experiments.



 

 




Development Status


SOVYN 1



85M

SOVYN 2



300M

SOVYN 2.3 Fast



~1B

Current work is focused on the SOVYN 2.3 generation.


Project Meridian


SOVYN 2.3 Fast
│
├── Language
│   ├── Korean
│   ├── English
│   └── Code
│
├── Architecture
│   ├── GQA
│   ├── QK-Norm
│   ├── RoPE
│   ├── SwiGLU
│   └── Local / Global Attention
│
├── Efficiency
│   ├── BF16
│   ├── Gradient Checkpointing
│   ├── 8-bit Optimizer
│   └── Tied Embeddings
│
└── Omni Research
    ├── Vision
    ├── Speech
    ├── Image Generation
    └── Speech Generation


Open Research

SOVYN aims to publish model artifacts whenever they are ready
and when licensing and technical conditions allow.


Weights

Model checkpoints
and releases.

Configs

Architecture
specifications.

Data

Dataset sources
and documentation.

Eval

Benchmark
results.

Reports

Technical
notes.


Models built for real work.

Open models  ·  Independent research  ·  Efficient intelligence




SOVYN Research



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