DeepSeek-Coder-V2-Lite-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 15.7B |
| License | DeepSeek License |
| Quantized By | bartowski |
| Model Type | Text Generation/Code Generation |
What is DeepSeek-Coder-V2-Lite-Instruct-GGUF?
DeepSeek-Coder-V2-Lite-Instruct-GGUF is a specialized coding language model that has been optimized through various quantization methods to provide efficient deployment options. The model comes in multiple GGUF formats, ranging from 17.09GB to 5.96GB in size, offering different trade-offs between quality and resource requirements.
Implementation Details
The model uses a specific prompt format and offers various quantization options using llama.cpp. The quantization methods include both traditional K-quants (Q8_0 to Q2_K) and innovative I-quants (IQ4_XS to IQ2_XS), each optimized for different use cases and hardware configurations.
- Multiple quantization options ranging from extremely high quality (Q8_0) to very compressed (IQ2_XS)
- Experimental variants with f16 for embed and output weights
- Support for different hardware configurations including CUDA, ROCm, and CPU
- Optimized prompt format for instruction-following tasks
Core Capabilities
- Code generation and understanding
- Flexible deployment options for various hardware configurations
- Memory-efficient operation through different quantization levels
- Support for both high-performance and resource-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its extensive range of quantization options, allowing users to choose the perfect balance between model quality and resource usage. It's specifically optimized for coding tasks and offers experimental variants with f16 precision for embed and output weights.
Q: What are the recommended use cases?
For optimal performance, users should choose quantization based on their hardware capabilities. For GPU deployment, select a model 1-2GB smaller than available VRAM. For maximum quality, consider combined RAM and VRAM capacity. K-quants are recommended for general use, while I-quants are better for lower quantization levels on CUDA/ROCm systems.
