Astra-v1-12B-GGUF
| Property | Value |
|---|---|
| Original Model | P0x0/Astra-v1-12B |
| Quantization Author | mradermacher |
| Model Size Range | 4.9GB - 13.1GB |
| Source Repository | https://huggingface.co/mradermacher/Astra-v1-12B-GGUF |
What is Astra-v1-12B-GGUF?
Astra-v1-12B-GGUF is a comprehensive collection of quantized versions of the original Astra-v1-12B model, optimized for different use cases and hardware configurations. The quantization process reduces the model's size while maintaining varying degrees of performance, offering users flexibility in choosing between file size and quality.
Implementation Details
The model comes in multiple quantization formats, each optimized for different use cases. The quantization types range from Q2_K (4.9GB) to Q8_0 (13.1GB), with several intermediate options providing different balances between size and quality.
- Q4_K_S and Q4_K_M variants (7.2GB and 7.6GB) are recommended for general use, offering good speed and quality
- Q6_K (10.2GB) provides very good quality with moderate size increase
- Q8_0 (13.1GB) represents the highest quality quantization available
- IQ4_XS (6.9GB) offers improved quality compared to similar-sized non-IQ quantizations
Core Capabilities
- Multiple quantization options for different hardware constraints
- Optimized performance-to-size ratios
- Compatible with standard GGUF file format
- Supports both static and weighted/imatrix quantization methods
Frequently Asked Questions
Q: What makes this model unique?
This model provides a comprehensive range of quantization options for the Astra-v1-12B model, allowing users to choose the optimal balance between model size and performance for their specific use case.
Q: What are the recommended use cases?
For most users, the Q4_K_S or Q4_K_M variants are recommended as they provide a good balance of speed and quality. For applications requiring maximum quality, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the smaller Q2_K or Q3_K_S variants.
