TheDrummer_Skyfall-36B-v2-GGUF
| Property | Value |
|---|---|
| Original Model | Skyfall-36B-v2 |
| Quantization Options | 25 variants (Q8_0 to IQ2_XS) |
| Size Range | 11.12GB - 39.23GB |
| Format | GGUF |
What is TheDrummer_Skyfall-36B-v2-GGUF?
TheDrummer_Skyfall-36B-v2-GGUF is a comprehensive collection of quantized versions of the Skyfall-36B-v2 model, optimized for various hardware configurations and use cases. The model uses imatrix quantization techniques to provide multiple compression levels while maintaining performance.
Implementation Details
The model offers 25 different quantization variants, each optimized for specific use cases. The quantizations range from the highest quality Q8_0 (39.23GB) to the most compressed IQ2_XS (11.12GB), with various intermediate options balancing quality and size.
- Supports standard prompt format with system prompts and instructions
- Implements advanced quantization techniques including embed/output weight optimizations
- Offers online repacking for ARM and AVX CPU inference in specific variants
- Uses SOTA techniques for maintaining usability even in highly compressed versions
Core Capabilities
- Multiple quantization options for different hardware configurations
- Optimized performance on both CPU and GPU implementations
- Special variants with Q8_0 embed and output weights for enhanced quality
- Compatible with LM Studio and any llama.cpp based project
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its extensive range of quantization options, allowing users to choose the perfect balance between model size and performance for their specific hardware setup. The implementation of both K-quants and I-quants provides flexibility for different acceleration backends.
Q: What are the recommended use cases?
For maximum quality, the Q6_K_L variant is recommended. For balanced performance, Q4_K_M is suggested as the default option. For systems with limited RAM, the I-quant variants (IQ3_XS, IQ2_XS) offer surprisingly good performance at smaller sizes.
