SmolLM2-360M-GGUF
| Property | Value |
|---|---|
| Parameter Count | 362M |
| License | CreativeML OpenRAIL-M |
| Language | English |
| Base Model | HuggingFaceTB/SmolLM2-360M |
What is SmolLM2-360M-GGUF?
SmolLM2-360M-GGUF is a compact language model optimized for efficient text generation, available in multiple GGUF quantization formats. This model represents a balanced approach between model size and performance, making it particularly suitable for resource-conscious applications.
Implementation Details
The model is available in four different quantization formats: F16 (726MB), Q4_K_M (271MB), Q5_K_M (290MB), and Q8_0 (386MB). Each format offers different trade-offs between memory efficiency, inference speed, and accuracy.
- F16: Full precision 16-bit floats for maximum accuracy
- Q4_K_M: 4-bit quantization for optimal memory efficiency
- Q5_K_M: Balanced 5-bit quantization
- Q8_0: 8-bit quantization for improved accuracy while maintaining efficiency
Core Capabilities
- Efficient text generation with minimal resource requirements
- Compatible with Ollama for easy deployment and usage
- Supports various quantization levels for different use-case requirements
- Optimized for English language tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's main strength lies in its efficient design and variety of quantization options, making it highly adaptable to different hardware configurations and performance requirements.
Q: What are the recommended use cases?
This model is ideal for applications requiring lightweight text generation capabilities, particularly in resource-constrained environments. It's especially suitable for development and testing scenarios where a balance between performance and resource usage is crucial.
