aya-expanse-8b-GGUF
| Property | Value |
|---|---|
| Original Model | CohereForAI/aya-expanse-8b |
| Quantization Types | F16 to Q2_K variants |
| Size Range | 3.08GB - 16.07GB |
| Author | bartowski |
What is aya-expanse-8b-GGUF?
aya-expanse-8b-GGUF is a comprehensive collection of quantized versions of the original aya-expanse-8b model, optimized using llama.cpp's imatrix quantization technology. This suite offers various compression levels to accommodate different hardware capabilities and memory constraints while maintaining model performance.
Implementation Details
The model comes in multiple quantization formats, from full F16 weights (16.07GB) down to highly compressed IQ2_M (3.08GB) versions. Each variant is carefully optimized using imatrix quantization, with special attention paid to embedding and output weights in certain versions.
- Specialized ARM optimizations with Q4_0_X_X variants for enhanced performance on ARM chips
- K-quant and I-quant variants offering different performance/size trade-offs
- Embed/output weight optimizations in XL and L variants using Q8_0 quantization
Core Capabilities
- Multiple quantization options for different RAM/VRAM configurations
- Optimized performance for various hardware architectures (CPU, GPU, ARM)
- Specialized variants for cuBLAS (Nvidia) and rocBLAS (AMD)
- Flexible deployment options from high-quality to resource-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
The model offers an exceptionally wide range of quantization options, allowing users to fine-tune the balance between model size and performance. The implementation includes cutting-edge techniques like I-quants and specialized ARM optimizations, making it highly versatile across different hardware configurations.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q8_0 variants if RAM allows. For balanced performance, Q4_K_M is recommended as the default choice. For resource-constrained environments, IQ3_M or Q3_K_M provide reasonable performance with minimal RAM requirements. ARM users should consider Q4_0_4_4 for optimal performance.
