calme-3.3-instruct-3b-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3.09B |
| Model Type | Instruction-tuned Language Model |
| Format | GGUF (Multiple Quantization Options) |
| Author | MaziyarPanahi |
What is calme-3.3-instruct-3b-GGUF?
calme-3.3-instruct-3b-GGUF is a specialized conversion of the original calme-3.3-instruct-3b model into the GGUF format, designed for efficient deployment and inference. This model represents a significant advancement in making large language models more accessible for local deployment, offering multiple quantization options from 2-bit to 8-bit precision to balance performance and resource usage.
Implementation Details
The model leverages the new GGUF format, which replaced the older GGML format in August 2023. It's specifically designed for compatibility with various deployment platforms and libraries, including llama.cpp, LM Studio, and text-generation-webui.
- Multiple quantization options (2-bit to 8-bit) for flexible deployment
- Optimized for local inference using the GGUF format
- Compatible with major deployment platforms and libraries
- Built on the Mistral architecture
Core Capabilities
- Text generation and instruction following
- Efficient local deployment with various precision options
- Cross-platform compatibility with major GGUF-supporting applications
- Balanced performance and resource usage through quantization options
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its versatility in deployment options, offering multiple quantization levels that allow users to balance between model size and performance. The GGUF format makes it particularly suitable for local deployment across various platforms and applications.
Q: What are the recommended use cases?
The model is ideal for local deployment scenarios where efficient resource usage is crucial. It's particularly well-suited for applications requiring instruction-following capabilities, text generation, and conversational AI, especially in environments where different precision levels might be needed for different hardware configurations.
