Q2.5-MS-Mistoria-72b-v2-GGUF
| Property | Value |
|---|---|
| Parameter Count | 72.7B |
| Model Type | GGUF Quantized Language Model |
| Primary Language | English |
| Author | mradermacher |
What is Q2.5-MS-Mistoria-72b-v2-GGUF?
This is a highly optimized GGUF-formatted variant of the Mistoria 72B model, offering multiple quantization options ranging from Q2 to Q8. The model represents a significant achievement in model compression while maintaining performance, with file sizes ranging from 29.9GB to 77.4GB depending on the quantization level.
Implementation Details
The model features multiple quantization variants optimized for different use-cases and hardware configurations. Notable variants include Q4_K_M and Q4_K_S which are recommended for general use, offering a good balance between performance and resource requirements. The Q8_0 variant provides the highest quality but requires more resources at 77.4GB.
- Multiple quantization options (Q2_K through Q8_0)
- File sizes ranging from 29.9GB to 77.4GB
- Includes both standard and IQ (Improved Quantization) variants
- Compatible with major LLM frameworks
Core Capabilities
- Efficient deployment with various memory footprint options
- Optimized for conversational applications
- Supports both standard and advanced quantization techniques
- Includes weighted/imatrix variants for enhanced performance
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options, allowing users to choose the optimal balance between model size and performance for their specific use case. The availability of both standard and IQ quantization variants provides additional flexibility.
Q: What are the recommended use cases?
The model is particularly well-suited for deployments where memory efficiency is crucial. The Q4_K_S and Q4_K_M variants are recommended for general use, while Q8_0 is ideal for scenarios requiring maximum quality. The Q2_K variant offers the smallest footprint at 29.9GB.
