Gemma 3-27B IT Abliterated GGUF
| Property | Value |
|---|---|
| Original Model | mlabonne/gemma-3-27b-it-abliterated |
| Format | GGUF (Various Quantizations) |
| Size Range | 8.44GB - 54.03GB |
| Author | bartowski |
What is mlabonne_gemma-3-27b-it-abliterated-GGUF?
This is a comprehensive collection of quantized versions of the Gemma 3-27B model, optimized using llama.cpp's imatrix quantization technology. The collection offers various compression levels to accommodate different hardware capabilities and use-cases, ranging from full BF16 weights (54.03GB) to highly compressed IQ2_XS format (8.44GB).
Implementation Details
The model utilizes advanced quantization techniques including K-quants and I-quants, with special optimizations for embed/output weights. Each variant is carefully balanced between model size, inference speed, and quality preservation.
- Uses llama.cpp release b4896 for quantization
- Implements online repacking for ARM and AVX CPU inference
- Supports various precision levels from BF16 to IQ2
- Special handling for embedding and output weights in certain variants
Core Capabilities
- Multiple quantization options for different hardware configurations
- Optimized performance for both GPU and CPU inference
- Support for vision capabilities through MMPROJ files
- Flexible deployment options through llama.cpp-based projects
Frequently Asked Questions
Q: What makes this model unique?
This model provides an extensive range of quantization options using state-of-the-art techniques, allowing users to choose the optimal balance between model size, performance, and quality for their specific hardware configuration.
Q: What are the recommended use cases?
For most general use cases, the Q4_K_M variant (16.55GB) is recommended as it provides a good balance of quality and size. For high-end systems, Q6_K_L (22.51GB) offers near-perfect quality, while for systems with limited RAM, the IQ2_XS (8.44GB) provides a usable experience with minimal resource requirements.
