Llama-2-Ko-7B
| Property | Value |
|---|---|
| Parameter Count | 6.86B |
| Model Type | Text Generation |
| Vocabulary Size | 46,336 tokens |
| Training Tokens | >40B |
| License | Not Specified |
What is llama-2-ko-7b?
Llama-2-Ko-7B is an advanced iteration of Meta's Llama 2 model, specifically optimized for Korean language processing. This model features an expanded vocabulary and has undergone additional pretraining with Korean corpus data. Built on the efficient Llama 2 architecture, it maintains the original model's capabilities while significantly improving Korean language understanding and generation.
Implementation Details
The model utilizes an optimized transformer architecture based on Llama-2, with several key improvements for Korean language processing. The vocabulary has been expanded from the original 32,000 to 46,336 tokens, incorporating specialized Korean vocabulary and merges. Training involved over 40B tokens with plans to extend to 200B tokens.
- Enhanced tokenization efficiency for Korean text
- Improved few-shot learning capabilities
- Optimized for both English and Korean language processing
- Uses BF16/F32 tensor types for computation
Core Capabilities
- Strong performance in Korean language understanding tasks
- Competitive results in COPA, HellaSwag, and BoolQ benchmarks
- Efficient tokenization of Korean text compared to base Llama-2
- Support for both zero-shot and few-shot learning scenarios
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized Korean language capabilities while maintaining the powerful base architecture of Llama-2. The expanded vocabulary and optimized tokenization make it particularly effective for Korean text processing tasks.
Q: What are the recommended use cases?
The model is well-suited for Korean language text generation, understanding, and analysis tasks. It performs particularly well in scenarios requiring comprehension of both Korean and English content, making it ideal for multilingual applications.
