SOVYN-300M-Cortex

SOVYN-300M-Cortex is a small Korean conversational model trained from scratch. It is not a Transformers-compatible checkpoint yet; it uses the custom PyTorch architecture in src/sovyn.

Current Status

• Parameters: about 300M
• Format: custom PyTorch checkpoint
• Tokenizer: SentencePiece BPE
• Context length: 512 tokens
• Weight dtype in checkpoint: bfloat16
• Main checkpoint: sovyn_300m_last.pt

This is an early experimental checkpoint. It can handle short Korean dialogue patterns, but it is not a broad knowledge model.

Quick Start

python -m venv .venv
.\.venv\Scripts\python.exe -m pip install torch sentencepiece pyyaml tqdm
.\.venv\Scripts\python.exe scripts\chat.py --checkpoint sovyn_300m_last.pt --tokenizer sovyn.model

Example:

user: 나 오늘 피곤해
sovyn: 많이 지쳤겠다. 지금은 잠깐 쉬어도 괜찮아.

Ollama-Compatible Local API

This repository includes an Ollama-compatible bridge, but the model is not a native GGUF Ollama model yet.

powershell -ExecutionPolicy Bypass -File scripts\start_ollama_bridge.ps1

Then call:

POST http://127.0.0.1:11435/api/chat

Files

• sovyn_300m_last.pt: model checkpoint
• sovyn.model, sovyn.vocab: SentencePiece tokenizer
• config.yaml: model and training config
• src/sovyn: custom PyTorch architecture and formatting/data helpers
• scripts/chat.py: local chat runner
• scripts/ollama_bridge.py: Ollama-compatible local API bridge

Notes

SOVYN-300M-Cortex was trained for short, natural Korean replies. The next major step is converting the architecture to a standard export format or writing a GGUF converter so it can be registered as a native Ollama model.