YandexGPT-5-Lite-8B-instruct
| Property | Value |
|---|---|
| Parameter Count | 8 Billion |
| Context Length | 32,000 tokens |
| Training Approach | SFT + RLHF |
| Model URL | huggingface.co/yandex/YandexGPT-5-Lite-8B-instruct |
What is YandexGPT-5-Lite-8B-instruct?
YandexGPT-5-Lite-8B-instruct is an instruction-tuned language model developed by Yandex, specifically designed for Russian language tasks. Built upon the YandexGPT 5 Lite Pretrain architecture, this model combines advanced instruction-following capabilities with extensive Russian cultural and factual knowledge. The model has demonstrated competitive performance against similar-sized models like Llama-3.1-8B-instruct and Qwen-2.5-7B-instruct.
Implementation Details
The model implements a sophisticated architecture with several key technical features. It utilizes a custom tokenization approach with sentencepiece, and employs a unique dialogue template structure where responses are generated after an "Ассистент:[SEP]" prompt. The model supports both HuggingFace Transformers and vLLM implementations, with additional support for llama.cpp and ollama through GGUF quantization.
- Custom tokenization with newline handling ([NL] tokens)
- 32k token context window
- Specialized chat template implementation
- Available in multiple deployment formats (Transformers, vLLM, GGUF)
Core Capabilities
- Strong performance in Russian language understanding and generation
- Advanced instruction-following abilities through RLHF training
- Competitive benchmark performance, particularly in Russian cultural context
- Flexible deployment options across different platforms
- Extended context handling capability (32k tokens)
Frequently Asked Questions
Q: What makes this model unique?
The model's primary uniqueness lies in its specialized Russian language capabilities and its robust instruction-following abilities, achieved through a combination of SFT and RLHF training. It's particularly notable for its extended context length and strong performance in Russian cultural and factual knowledge tasks.
Q: What are the recommended use cases?
The model is well-suited for Russian language processing tasks, instruction-following applications, and scenarios requiring deep understanding of Russian cultural context. It's particularly effective in dialogue-based applications and can be deployed across various platforms thanks to its multiple format support.
