KoGPT
| Property | Value |
|---|---|
| Parameter Count | 6.17B parameters |
| Architecture | Transformer-based with 28 layers |
| Context Length | 2,048 tokens |
| License | Apache 2.0 (code), CC-BY-NC-ND 4.0 (weights) |
| Author | KakaoBrain |
What is kogpt?
KoGPT is a state-of-the-art Korean language model developed by KakaoBrain, designed specifically for Korean text processing and generation. This powerful model contains 6.17B parameters and is built on the transformer architecture, making it one of the most advanced Korean language models available.
Implementation Details
The model features a sophisticated architecture with 28 layers, 4,096 hidden dimensions, and 16 attention heads. It uses Rotary Position Embedding (RoPE) for positional encoding and supports a context window of 2,048 tokens. The model is available in both float32 and float16 precision versions to accommodate different hardware requirements.
- 4,096 model dimensions with 16,384 feed-forward dimensions
- 16 attention heads with 256 dimensions per head
- 64,512 vocabulary size
- Supports both full precision and half-precision inference
Core Capabilities
- Advanced Korean text generation and understanding
- High performance on Korean classification tasks
- Strong performance on sentiment analysis (NSMC)
- Excellent results on news article classification (YNAT)
- Semantic textual similarity analysis (KLUE-STS)
Frequently Asked Questions
Q: What makes this model unique?
KoGPT stands out for its specialized focus on Korean language processing, achieving impressive performance metrics while using fewer parameters than comparable models. It demonstrates strong capabilities in various Korean NLP tasks and offers both float32 and float16 versions for different deployment scenarios.
Q: What are the recommended use cases?
The model is best suited for Korean text classification, searching, summarizing, and generation tasks. It's particularly effective for applications requiring deep understanding of Korean language nuances, though users should be aware of potential limitations with non-Korean text or specific Korean dialects not well-represented in the training data.
