Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-GGUF
| Property | Value |
|---|---|
| Model Size | 14B parameters |
| Format | GGUF |
| Author | mradermacher |
| Source Model | Josiefied-Qwen2.5-14B-Instruct-abliterated-v4 |
What is Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-GGUF?
This is a quantized version of the Josiefied-Qwen2.5-14B model, optimized for efficient deployment through various compression levels. The model provides multiple quantization options, allowing users to balance between model size and performance based on their specific needs.
Implementation Details
The model offers several quantization variants, from highly compressed Q2_K at 5.9GB to high-quality Q8_0 at 15.8GB. Notable implementations include the recommended Q4_K_S and Q4_K_M variants, which offer a good balance between speed and quality at 8.7GB and 9.1GB respectively.
- Multiple quantization options (Q2_K through Q8_0)
- Size ranges from 5.9GB to 15.8GB
- Includes both standard and IQ-quant variants
- Optimized for different performance requirements
Core Capabilities
- Efficient deployment with various compression levels
- Fast inference with Q4_K variants
- High-quality output with Q6_K and Q8_0 variants
- Flexible size options for different hardware constraints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its range of quantization options, allowing users to choose the optimal balance between model size and performance. The availability of both standard and IQ-quant variants provides additional flexibility for different use cases.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they offer a good balance of speed and quality. For highest quality requirements, the Q8_0 variant is recommended, while resource-constrained environments may benefit from the smaller Q2_K or Q3_K_S variants.
