Mistral-Small-3.1-24B-Instruct-2503-MAX-NEO-Imatrix-GGUF
| Property | Value |
|---|---|
| Base Model | Mistral-Small-3.1-24B-Instruct-2503 |
| Context Length | 128k tokens |
| Format | GGUF |
| Author | DavidAU |
| Repository | Hugging Face |
What is Mistral-Small-3.1-24B-Instruct-2503-MAX-NEO-Imatrix-GGUF?
This is an enhanced version of Mistral's 24B parameter instruction-tuned model, featuring specialized quantization techniques and a custom "Neo Imatrix" dataset. The model combines BF16 precision for embed and output tensors with an innovative quantization approach to maximize performance while maintaining quality.
Implementation Details
The model implements two key technical innovations: "MAXED" quantization, which uses BF16 precision for critical tensors, and the "NEO IMATRIX" dataset, a custom-built collection that enhances the model's understanding and generation capabilities. It supports various quantization levels from IQ1 to Q8_0, with recommended settings for different use cases.
- Enhanced BF16 precision for embed and output tensors
- Custom Neo Imatrix dataset for improved concept understanding
- Multiple quantization options (IQ3s/IQ4XS/IQ4NL recommended for creative use)
- 128k context window
- Uncensored output capability
Core Capabilities
- High-quality text generation with enhanced creative abilities
- Strong instruction following and reasoning capabilities
- Flexible quantization options for different hardware requirements
- Extended context handling up to 128k tokens
- Improved concept understanding through Neo Imatrix dataset
Frequently Asked Questions
Q: What makes this model unique?
The combination of MAXED quantization and Neo Imatrix dataset sets this model apart, offering enhanced performance while maintaining high-quality outputs. The model particularly excels in creative tasks when used with specific quantization settings (IQ3s/IQ4XS/IQ4NL).
Q: What are the recommended use cases?
The model is optimized for text generation tasks, particularly creative writing and detailed responses. Different quantization options allow for optimization based on use case: IQ3s/IQ4XS/IQ4NL for creative uses, Q5s/Q6/Q8 for general usage, and Q4_0/Q5_0 for mobile devices.
