Josiefied-Qwen2.5-3B-Instruct-abliterated-v1-GGUF
| Property | Value |
|---|---|
| Base Model | Qwen2.5-3B-Instruct |
| Format | GGUF |
| Author | mradermacher |
| Source | Hugging Face |
What is Josiefied-Qwen2.5-3B-Instruct-abliterated-v1-GGUF?
This model represents a specialized quantized version of the Josiefied-Qwen2.5-3B-Instruct model, optimized for efficient deployment through various compression levels. It offers multiple quantization options ranging from highly compressed (Q2_K at 1.5GB) to full precision (f16 at 6.9GB), allowing users to balance performance and resource requirements.
Implementation Details
The model is available in multiple quantization formats, each optimized for different use cases. The implementation includes both static quantizations and weighted/imatrix variants, with specific optimizations for deployment efficiency.
- Multiple quantization options from Q2_K to f16
- Optimized formats for different performance requirements
- IQ-quants available for enhanced quality at lower sizes
- Comprehensive range of compression ratios (1.5GB to 6.9GB)
Core Capabilities
- Fast inference with Q4_K_S and Q4_K_M variants (recommended)
- High-quality output with Q6_K and Q8_0 quantizations
- Flexible deployment options for various hardware configurations
- Optimized memory usage while maintaining model performance
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, allowing users to choose the optimal balance between model size and performance. The availability of IQ-quants provides superior quality compared to traditional quantization methods of similar sizes.
Q: What are the recommended use cases?
For most applications, the Q4_K_S (2.1GB) and Q4_K_M (2.2GB) variants are recommended as they offer a good balance of speed and quality. For highest quality requirements, the Q8_0 (3.7GB) variant is recommended, while Q2_K (1.5GB) is suitable for extremely resource-constrained environments.
