Mistral-Small-24B-Instruct-2501-abliterated-i1-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Base Model | Mistral-Small-24B-Instruct-2501 |
| Format | GGUF (Various Quantizations) |
| Model URL | https://huggingface.co/mradermacher/Mistral-Small-24B-Instruct-2501-abliterated-i1-GGUF |
What is Mistral-Small-24B-Instruct-2501-abliterated-i1-GGUF?
This is a specialized quantized version of the Mistral-Small-24B model, offering multiple GGUF variants optimized for different use cases. The model provides various quantization options ranging from extremely compressed 5.4GB versions to high-quality 19.4GB implementations, allowing users to choose based on their specific needs for speed, quality, and resource constraints.
Implementation Details
The model features both weighted and imatrix quantizations, with multiple compression levels available. The quantization types include IQ (Improved Quantization) variants and traditional quantization methods, each optimized for specific use cases.
- Size range: 5.4GB (i1-IQ1_S) to 19.4GB (i1-Q6_K)
- Multiple quantization types: IQ1, IQ2, IQ3, Q2_K, Q3_K, Q4_K, Q5_K, Q6_K
- Optimized variants for different performance/size trade-offs
Core Capabilities
- Flexible deployment options with various size/quality trade-offs
- Recommended Q4_K_M variant (14.4GB) for optimal performance
- IQ-quants often provide better quality than similar-sized traditional quants
- Suitable for resource-constrained environments with smaller variants
Frequently Asked Questions
Q: What makes this model unique?
This model provides an extensive range of quantization options, allowing users to choose the perfect balance between model size and performance. The implementation includes innovative IQ (Improved Quantization) variants that often outperform traditional quantization methods at similar sizes.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M variant (14.4GB) is recommended as it offers a good balance of speed and quality. For resource-constrained environments, the IQ variants provide quality alternatives at smaller sizes. The Q6_K variant (19.4GB) offers quality comparable to static quantization for users prioritizing performance.
