YandexGPT-5-Lite-8B-pretrain
| Property | Value |
|---|---|
| Parameter Count | 8 Billion |
| Context Length | 32,000 tokens |
| Architecture | LLaMA-like |
| Model URL | https://huggingface.co/yandex/YandexGPT-5-Lite-8B-pretrain |
What is YandexGPT-5-Lite-8B-pretrain?
YandexGPT-5-Lite-8B-pretrain is a powerful language model developed by Yandex, featuring 8 billion parameters and an impressive 32k token context length. The model underwent a two-phase training process, initially trained on 15T tokens of predominantly Russian and English text, followed by a specialized "Powerup" phase with 320B tokens of high-quality data.
Implementation Details
The model features a LLaMA-like architecture and was trained in two distinct phases. The first phase focused on general training with a diverse dataset comprising 60% web pages, 15% code, 10% mathematics, and various other specific data. The second "Powerup" phase utilized a carefully curated dataset including 25% web pages, 19% mathematics, 18% code, and 18% educational content.
- Optimized tokenizer for Russian language processing
- 32k token context length (equivalent to approximately 48k tokens in Qwen-2.5)
- Compatible with major fine-tuning frameworks
- Supports both HF Transformers and vLLM implementations
Core Capabilities
- Advanced Russian and English language processing
- Extended context understanding (32k tokens)
- Code generation and analysis
- Mathematical computation and reasoning
- Educational content processing
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive features include its optimized Russian language processing capabilities, extensive context length, and specialized two-phase training approach. The tokenizer efficiency for Russian language makes it particularly valuable for Russian-language applications.
Q: What are the recommended use cases?
The model is well-suited for various applications including code generation, mathematical analysis, educational content processing, and general language understanding tasks. It's particularly effective for applications requiring extended context understanding and Russian language processing.
