c4ai-command-a-03-2025-GGUF
| Property | Value |
|---|---|
| License | CC-BY-NC + C4AI's Acceptable Use Policy |
| Context Length | 256,000 tokens |
| Supported Languages | 23 languages |
| Model URL | huggingface.co/lmstudio-community/c4ai-command-a-03-2025-GGUF |
What is c4ai-command-a-03-2025-GGUF?
c4ai-command-a-03-2025-GGUF is a GGUF-quantized version of the original c4ai-command model, optimized by bartowski using llama.cpp release b4877. This powerful multilingual model is designed for diverse applications including conversation, retrieval-augmented generation (RAG), tool use, and coding tasks.
Implementation Details
The model features an impressive context window of 256,000 tokens, making it suitable for processing lengthy documents and conversations. It has been quantized using the GGUF format, allowing for efficient deployment and reduced memory footprint while maintaining performance.
- GGUF quantization based on llama.cpp
- Extensive language support covering 23 major languages
- Optimized for multiple use cases including RAG and coding
- Community-supported implementation
Core Capabilities
- Multilingual Processing: Supports 23 languages including English, French, Spanish, Chinese, and more
- Extended Context Window: 256k token context length for handling long-form content
- Task Versatility: Equipped for conversation, RAG, tool use, and coding tasks
- Code Generation: Specialized capabilities for programming tasks
- Tool Integration: Built-in support for tool use and automation
Frequently Asked Questions
Q: What makes this model unique?
The model's combination of extensive language support (23 languages), large context window (256k tokens), and versatile capabilities across conversation, RAG, and coding makes it particularly valuable for diverse applications. The GGUF quantization ensures efficient deployment while maintaining functionality.
Q: What are the recommended use cases?
This model is ideal for multilingual applications, long-form content processing, coding assistance, and RAG implementations. It's particularly suited for scenarios requiring tool integration and handling complex conversations across multiple languages.
