Tesslate Synthia-S1-27b-GGUF
| Property | Value |
|---|---|
| Original Model | Tesslate/Synthia-S1-27b |
| Quantization Types | Multiple (Q2-Q8) |
| Size Range | 7.69GB - 54.03GB |
| Model Hub | HuggingFace |
What is Tesslate_Synthia-S1-27b-GGUF?
Tesslate_Synthia-S1-27b-GGUF is a comprehensive collection of GGUF quantized versions of the original Synthia-S1-27b model, offering various compression levels to accommodate different hardware capabilities and performance requirements. The model uses llama.cpp's imatrix quantization technology to maintain optimal performance while reducing model size.
Implementation Details
The model comes in multiple quantization formats, from the full BF16 weights (54.03GB) down to highly compressed IQ2_XXS (7.69GB) versions. Each quantization level offers different trade-offs between model size, quality, and performance. The implementation includes specialized versions with Q8_0 quantization for embedding and output weights in certain variants for enhanced performance.
- Utilizes llama.cpp release b5035 for quantization
- Implements online repacking for ARM and AVX CPU inference in specific formats
- Features both K-quants and I-quants for different use cases
- Supports various hardware configurations from high-end to resource-constrained systems
Core Capabilities
- Multiple quantization options for different hardware constraints
- Optimized performance for both CPU and GPU implementations
- Special handling of embedding and output weights for enhanced quality
- Support for online repacking in specific formats
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, allowing users to choose the perfect balance between model size, quality, and performance for their specific hardware setup. The implementation of both K-quants and I-quants, along with specialized handling of embedding weights, makes it highly versatile.
Q: What are the recommended use cases?
For users with ample VRAM/RAM, the Q6_K_L or Q5_K_M variants are recommended for optimal quality. For resource-constrained systems, the IQ4_XS or Q4_K_M variants offer a good balance. The I-quant versions are particularly suitable for GPU implementations using cuBLAS or rocBLAS.
