NuExtract-1.5-smol-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.71B |
| Model Type | Text Generation |
| Quantization Options | 2-bit to 8-bit precision |
| Author | MaziyarPanahi (Quantized) / numind (Original) |
What is NuExtract-1.5-smol-GGUF?
NuExtract-1.5-smol-GGUF is a quantized version of the original NuExtract-1.5-smol model, specifically optimized for efficient local deployment using the GGUF format. This model represents a significant advancement in making large language models more accessible for local deployment, offering various quantization options from 2-bit to 8-bit precision to balance performance and resource requirements.
Implementation Details
The model is implemented using the GGUF format, which is the successor to GGML and is designed for optimal performance in local environments. It supports multiple quantization levels, allowing users to choose the best trade-off between model size and accuracy for their specific use case.
- Multiple quantization options (2-bit to 8-bit)
- GGUF format optimization for local deployment
- Compatible with various client applications and libraries
- Optimized for efficient memory usage
Core Capabilities
- Text generation with 1.71B parameter architecture
- Efficient local deployment through GGUF format
- Compatible with popular frameworks like llama.cpp
- Flexible deployment options across different platforms
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient implementation in GGUF format and multiple quantization options, making it highly versatile for different hardware configurations and use cases. The availability of different bit-precision options allows users to optimize for their specific needs.
Q: What are the recommended use cases?
The model is particularly well-suited for local deployment scenarios where efficient resource usage is crucial. It's ideal for applications requiring text generation capabilities while maintaining reasonable performance on consumer hardware.
