| Property | Value |
|---|---|
| Parameter Count | 3.82B |
| Model Type | GGUF Compressed |
| Context Length | 128k tokens |
| Author | PrunaAI |
What is Phi-3-mini-128k-instruct-GGUF-Imatrix-smashed?
This model is a compressed version of Microsoft's Phi-3-mini, optimized using PrunaAI's compression techniques and converted to the efficient GGUF format. It maintains the impressive 128k context window while reducing resource requirements through various quantization options.
Implementation Details
The model offers multiple quantization formats, from high-quality Q5_K_M to very lightweight Q2_K, allowing users to balance between performance and resource usage. It uses WikiText for calibration when required by the compression method and implements the GGUF format for optimal compatibility with various deployment options.
- Multiple quantization options (Q5_K_M to Q2_K)
- Optimized for both CPU and GPU inference
- Compatible with llama.cpp and popular frameworks
- Extended 128k context window support
Core Capabilities
- Long-context understanding (128k tokens)
- Instruction-following capabilities
- Efficient inference on various hardware configurations
- Flexible deployment options (local or server-based)
Frequently Asked Questions
Q: What makes this model unique?
The model combines the capabilities of Phi-3-mini with extended context length and efficient compression, making it suitable for deployment in resource-constrained environments while maintaining functionality.
Q: What are the recommended use cases?
This model is ideal for applications requiring long-context understanding, instruction following, and efficient deployment, particularly where resource optimization is crucial.