Deepseeker-Kunou-Qwen2.5-14b-i1-GGUF
| Property | Value |
|---|---|
| Original Model | Deepseeker-Kunou-Qwen2.5-14b |
| Quantization Types | Multiple GGUF formats (IQ1-Q6_K) |
| Size Range | 3.7GB - 12.2GB |
| Author | mradermacher |
| Model Hub | Hugging Face |
What is Deepseeker-Kunou-Qwen2.5-14b-i1-GGUF?
This is a specialized quantized version of the Deepseeker-Kunou-Qwen2.5-14b model, offering various compression formats optimized for different use cases. The model provides weighted/imatrix quantizations that enable users to balance between model size, performance, and quality requirements.
Implementation Details
The model implements several quantization techniques, ranging from IQ1 to Q6_K formats. Each format offers different trade-offs:
- IQ-quants (IQ1-IQ4): Innovative matrix-based quantization offering better quality than traditional methods
- K-quants (Q2_K-Q6_K): Standard quantization methods with varying compression ratios
- Size options from ultra-compressed (3.7GB) to high-quality (12.2GB)
- Includes optimized versions like Q4_K_M (9.1GB) recommended for balanced performance
Core Capabilities
- Multiple compression options suitable for different hardware configurations
- Optimized for memory-constrained environments
- IQ-quants often provide better quality than similar-sized traditional quants
- Q4_K_M variant offers optimal speed/quality balance
- Q6_K provides near-original model quality at reduced size
Frequently Asked Questions
Q: What makes this model unique?
The model offers a comprehensive range of quantization options, including innovative IQ-quants that often outperform traditional quantization methods at similar sizes. It's specifically designed to make the powerful Deepseeker-Kunou-Qwen2.5-14b accessible on various hardware configurations.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M (9.1GB) variant is recommended as it provides a good balance of speed and quality. For systems with limited resources, the IQ3 variants offer reasonable quality at smaller sizes. The Q6_K variant is ideal for users requiring near-original model quality.
