Q2.5-MS-Mistoria-72b-v2-i1-GGUF
| Property | Value |
|---|---|
| Parameter Count | 72.7B |
| Model Type | GGUF Quantized Language Model |
| Author | mradermacher |
| Base Model | Steelskull/Q2.5-MS-Mistoria-72b-v2 |
What is Q2.5-MS-Mistoria-72b-v2-i1-GGUF?
This is a highly optimized quantized version of the Mistoria 72B model, specifically designed for efficient deployment and inference. The model offers multiple quantization variants ranging from 22.8GB to 64.4GB, allowing users to balance between model size, speed, and quality based on their specific needs.
Implementation Details
The model implements various quantization techniques, including IQ (Integer Quantization) and standard quantization methods. It features different compression levels marked by suffixes like IQ1_S through Q6_K, each offering different trade-offs between model size and performance.
- Multiple quantization options from IQ1 to Q6_K
- Size variants ranging from 22.8GB to 64.4GB
- Optimized for different hardware configurations
- Implements imatrix quantization techniques
Core Capabilities
- Efficient inference with reduced memory footprint
- Multiple compression options for different use cases
- Maintains model quality while reducing size
- Compatible with standard GGUF implementations
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options, particularly the IQ variants which often provide better quality than similar-sized non-IQ quantized versions. The Q4_K_M variant (47.5GB) is specifically recommended for its optimal balance of speed and quality.
Q: What are the recommended use cases?
For users with limited resources, the IQ2 and IQ3 variants offer good performance at smaller sizes. For production environments, the Q4_K_M variant is recommended as it provides an optimal balance of speed, quality, and size. The Q6_K variant is suitable for users requiring maximum quality comparable to static quantization.
