CohereForAI Command-A-03-2025 GGUF
| Property | Value |
|---|---|
| Original Model | CohereForAI/c4ai-command-a-03-2025 |
| Quantization Framework | llama.cpp (b4877) |
| Size Range | 26.83GB - 118.01GB |
| Language Support | 22+ languages including English, French, Spanish, etc. |
| Author | bartowski |
What is CohereForAI_c4ai-command-a-03-2025-GGUF?
This is a comprehensive collection of quantized versions of Cohere's command-a-03-2025 model, optimized for different hardware configurations and use cases. The model uses imatrix quantization techniques to provide various compression levels while maintaining different quality-performance tradeoffs. It's designed to run efficiently using llama.cpp and supports a wide range of languages with a June 2024 knowledge cutoff.
Implementation Details
The model offers 26 different quantization variants, ranging from the highest quality Q8_0 (118.01GB) to the most compressed IQ1_M (26.83GB). Each variant uses specific quantization techniques including K-quants and I-quants, optimized for different hardware architectures including ARM and AVX systems.
- Advanced quantization methods including Q8_0, Q6_K, Q5_K, Q4_K, Q3_K, and IQ series
- Support for online weight repacking for ARM and AVX CPU inference
- Specialized variants with Q8_0 quantization for embed and output weights
- Compatibility with LM Studio and any llama.cpp-based project
Core Capabilities
- Multilingual processing in 22+ languages
- Contextual safety mode with content filtering
- Markdown and LaTeX formatting support
- Conversational AI with follow-up questions
- Code generation with explanations
- Step-by-step reasoning capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model offers an unprecedented range of quantization options, allowing users to balance quality and resource requirements precisely. It's particularly notable for its implementation of both K-quants and I-quants, providing optimized performance across different hardware platforms.
Q: What are the recommended use cases?
For most users, the Q4_K_M (67.14GB) variant is recommended as the default choice. Users with limited RAM should consider the Q3_K series or I-quants, while those prioritizing quality should opt for Q6_K or Q5_K variants. GPU users should choose a model size 1-2GB smaller than their available VRAM for optimal performance.
