DeepSeek-V2.5-GGUF
| Property | Value |
|---|---|
| Parameter Count | 236B |
| License | DeepSeek License |
| Base Model | deepseek-ai/DeepSeek-V2.5 |
| Quantization | Multiple GGUF formats |
What is DeepSeek-V2.5-GGUF?
DeepSeek-V2.5-GGUF is a comprehensive quantized version of the DeepSeek-V2.5 language model, offering various compression levels to accommodate different hardware configurations. This implementation provides 17 different quantization options ranging from 250GB to 52GB in file size, making it adaptable to various computing environments.
Implementation Details
The model utilizes llama.cpp's advanced quantization techniques, including both traditional K-quants and newer I-quants. It's optimized for text generation tasks and implements a specific prompt format: <｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜>
- Multiple quantization options from Q8_0 (highest quality) to IQ1_M (lowest size)
- Specialized versions with Q8_0 embedding weights for enhanced performance
- Compatible with various hardware configurations including CUDA, ROCm, and CPU
Core Capabilities
- High-quality text generation with varying performance-size tradeoffs
- Support for system prompts and structured conversations
- Optimized for both GPU and CPU inference
- Flexible deployment options based on available hardware resources
Frequently Asked Questions
Q: What makes this model unique?
This implementation offers an unprecedented range of quantization options for the DeepSeek-V2.5 model, including cutting-edge I-quants that provide better performance for smaller sizes, especially on CUDA and ROCm systems.
Q: What are the recommended use cases?
For optimal performance, it's recommended to use Q6_K or Q5_K_M variants for high-quality results, while Q4_K_M offers a good balance of quality and size. For systems with limited resources, the IQ3_M and IQ2_M variants provide surprisingly usable performance at reduced sizes.
