c4ai-command-r-08-2024-GGUF
| Property | Value |
|---|---|
| Parameter Count | 32.3B |
| License | CC-BY-NC-4.0 |
| Supported Languages | 10 (including English, French, German, Spanish, etc.) |
| Author | bartowski |
What is c4ai-command-r-08-2024-GGUF?
c4ai-command-r-08-2024-GGUF is a sophisticated multilingual language model offering various GGUF quantizations of the original CohereForAI model. It's designed to provide flexible deployment options across different hardware configurations while maintaining high performance standards.
Implementation Details
The model comes in multiple quantization formats ranging from full F16 (64.60GB) to highly compressed IQ2_XS (10.31GB) versions, each optimized for specific use cases and hardware constraints. The implementation utilizes llama.cpp's latest quantization techniques with imatrix optimization.
- Multiple quantization options for different performance/size trade-offs
- Specialized versions with Q8_0 embed/output weights for enhanced quality
- Support for both CPU and GPU deployment
- Optimized formats for ARM and CPU inference
Core Capabilities
- Multilingual support across 10 major languages
- Text generation with controllable output quality
- Flexible deployment options for various hardware configurations
- Optimized performance on both CPU and GPU platforms
Frequently Asked Questions
Q: What makes this model unique?
The model's standout feature is its extensive range of quantization options, allowing users to choose the perfect balance between model size and performance for their specific hardware setup. It also maintains high-quality output across multiple languages while offering various optimization levels.
Q: What are the recommended use cases?
For maximum performance, choose a quantization version 1-2GB smaller than your GPU's VRAM. For optimal quality, select a version that fits within your combined system RAM and GPU VRAM. K-quants are recommended for general use, while I-quants are better for specific hardware configurations, particularly with cuBLAS or rocBLAS.
