Gemma-2-27b-it-GGUF
| Property | Value |
|---|---|
| Parameter Count | 27.2B |
| License | Gemma License |
| Base Model | google/gemma-2-27b-it |
| Quantization Types | Multiple (F32 to IQ2) |
What is gemma-2-27b-it-GGUF?
Gemma-2-27b-it-GGUF is a comprehensive suite of quantized versions of Google's Gemma 2 27B instruction-tuned language model, optimized for efficient deployment using the GGUF format. Created by bartowski, this collection offers various compression levels to accommodate different hardware configurations while maintaining model performance.
Implementation Details
The model utilizes llama.cpp release b3389 for quantization and features imatrix-based quantization options ranging from full F32 precision (108.91GB) down to highly compressed IQ2_M (9.40GB) variants. Each quantization level offers different trade-offs between model size and performance quality.
- Supports various quantization types including Q8_0, Q6_K, Q5_K, Q4_K, and innovative IQ series
- Implements specific prompt format with start_of_turn and end_of_turn tokens
- Features specialized quantization for embed and output weights in certain variants
- Optimized for different hardware configurations (CPU, GPU, Apple Metal)
Core Capabilities
- Text generation with instruction-following capabilities
- Flexible deployment options across different hardware configurations
- Multiple compression levels for various memory constraints
- Support for both traditional K-quants and newer I-quants
- Compatibility with major acceleration frameworks (cuBLAS, rocBLAS)
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 and performance for their specific hardware setup. The inclusion of both traditional K-quants and newer I-quants provides flexibility for different use cases.
Q: What are the recommended use cases?
For optimal performance, users should choose quantization based on their available hardware. For GPU deployment, select a model 1-2GB smaller than available VRAM. For maximum quality, consider combined RAM and VRAM capacity. Q4_K_M is recommended as a balanced default, while I-quants are preferred for lower quantization levels on cuBLAS/rocBLAS systems.
