DeepSeek-Coder-V2-Instruct-0724-GGUF
| Property | Value |
|---|---|
| Original Model | DeepSeek-Coder-V2-Instruct-0724 |
| Quantization Types | Multiple (Q8_0 to IQ1_M) |
| Size Range | 52.68GB - 250.62GB |
| Author | bartowski |
What is DeepSeek-Coder-V2-Instruct-0724-GGUF?
This is a comprehensive collection of quantized versions of the DeepSeek Coder V2 model, specifically optimized for different hardware configurations and use cases. The quantizations were performed using llama.cpp release b3715 with imatrix options, offering various tradeoffs between model size, quality, and performance.
Implementation Details
The model comes in multiple quantization formats, each serving different needs:
- Q8_0 (250.62GB): Highest quality quantization, suitable for cases requiring maximum accuracy
- Q6_K (193.54GB): Very high quality, near-original model performance
- Q4_K_M (142.45GB): Recommended default for most use cases
- IQ4_XS (125.56GB): Efficient balance of size and performance
- Various lower quantization options down to IQ1_M for minimal resource requirements
Core Capabilities
- Supports multiple hardware configurations including CUDA, ROCm, and CPU
- Specialized versions for ARM chips with Q4_0_X_X quantization
- Flexible deployment options with split file support for large models
- Optimized embed/output weights in certain variants for improved performance
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options using state-of-the-art techniques, allowing users to select the perfect balance between model size, quality, and performance for their specific hardware setup.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant is recommended as a default choice. Users with limited RAM should consider I-quants for better performance at smaller sizes, while those requiring maximum quality should opt for Q6_K or Q8_0 variants if hardware permits.
