YandexGPT-5-Lite-8B-pretrain-Q4_K_M-GGUF
| Property | Value |
|---|---|
| Original Model | yandex/YandexGPT-5-Lite-8B-pretrain |
| Format | GGUF (4-bit quantized) |
| Size | 8B parameters |
| Author | blues-alex |
| Repository | Hugging Face |
What is YandexGPT-5-Lite-8B-pretrain-Q4_K_M-GGUF?
This is a converted and optimized version of Yandex's 8B parameter language model, specifically adapted for efficient deployment using llama.cpp. The model has been quantized to 4-bit precision (Q4_K_M) and converted to the GGUF format, making it more memory-efficient while maintaining reasonable performance.
Implementation Details
The model utilizes the GGUF format, which is optimized for local deployment through llama.cpp. It can be easily integrated using either the CLI or server mode, with support for various hardware configurations including CPU and GPU acceleration.
- 4-bit quantization for reduced memory footprint
- Compatible with llama.cpp's latest GGUF format
- Supports context length of 2048 tokens
- Direct integration with Hugging Face repositories
Core Capabilities
- Efficient local deployment through llama.cpp
- Support for both CLI and server implementations
- Cross-platform compatibility (Linux, MacOS)
- Hardware acceleration support (CPU, CUDA)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization while maintaining the capabilities of the original YandexGPT-5-Lite model, making it particularly suitable for local deployment on consumer hardware.
Q: What are the recommended use cases?
The model is ideal for users who want to run YandexGPT locally with minimal resource requirements while maintaining reasonable performance. It's particularly suited for development, testing, and production environments where efficiency is crucial.
