BlackSheep-Qwen-14B-i1-GGUF
| Property | Value |
|---|---|
| Base Model | BlackSheep-Qwen-14B |
| Model Type | GGUF Quantized |
| Author | mradermacher |
| Size Range | 3.7GB - 12.2GB |
| Source | Hugging Face Repository |
What is BlackSheep-Qwen-14B-i1-GGUF?
BlackSheep-Qwen-14B-i1-GGUF is a comprehensive collection of weighted/imatrix quantized versions of the original BlackSheep-Qwen-14B model. This implementation provides various compression levels optimized for different use cases, ranging from lightweight 3.7GB versions to high-quality 12.2GB implementations.
Implementation Details
The model offers multiple quantization types, each optimized for different scenarios:
- IQ-quants (IQ1_S through IQ4_NL) offering balanced performance
- Standard Q-quants (Q2_K through Q6_K) for traditional quantization approaches
- Special attention to size/quality trade-offs with options like Q4_K_M (9.1GB) recommended for optimal performance
- imatrix implementations for enhanced quality at similar file sizes
Core Capabilities
- Multiple compression levels suitable for various hardware configurations
- Optimized weighted quantization for better performance retention
- Options ranging from desperate use cases (3.7GB) to near-original quality (12.2GB)
- Special IQ-quant implementations often outperforming similar-sized standard quantizations
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options, particularly the imatrix implementations that often provide better quality than traditional quantization at similar file sizes. It offers a carefully balanced set of options from extremely compressed (3.7GB) to high-quality (12.2GB) versions.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M (9.1GB) version is recommended as it provides a good balance of speed and quality. For resource-constrained environments, the IQ3 series offers reasonable quality at smaller sizes. The Q6_K version (12.2GB) is recommended for cases requiring near-original model quality.
