SmolLM2-1.7B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.71B |
| License | Apache 2.0 |
| Language | English |
| Base Model | HuggingFaceTB/SmolLM2-1.7B-Instruct |
What is SmolLM2-1.7B-Instruct-GGUF?
SmolLM2-1.7B-Instruct-GGUF is a specialized instruction-tuned language model that provides efficient deployment options through various GGUF quantization levels. This model represents a balance between computational efficiency and performance, offering multiple versions optimized for different use cases.
Implementation Details
The model is available in multiple GGUF formats, each optimized for different scenarios: F16 (3.42GB) for maximum accuracy, Q4_K_M (1.06GB) for memory efficiency, Q5_K_M (1.23GB) for balanced performance, and Q8_0 (1.82GB) for moderate accuracy with reasonable size. The model is built on the LLaMa architecture and is specifically designed for instruction-following tasks.
- Full precision F16 version for highest accuracy
- Q4 quantization for minimal memory footprint
- Q5 quantization offering balanced performance
- Q8 quantization for better accuracy while maintaining efficiency
Core Capabilities
- Instruction-following and conversational tasks
- Efficient deployment with multiple quantization options
- Compatible with Ollama for easy deployment
- Text generation and natural language processing
Frequently Asked Questions
Q: What makes this model unique?
The model's distinguishing feature is its variety of quantization options that allow users to choose between performance and efficiency, making it suitable for different deployment scenarios while maintaining instruction-following capabilities.
Q: What are the recommended use cases?
This model is ideal for applications requiring instruction-following capabilities with resource constraints, particularly suitable for deployment on consumer hardware through Ollama integration.
