SmolLM2-360M-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 362M |
| License | Apache 2.0 |
| Format | GGUF (llama.cpp compatible) |
| Language | English |
| Base Model | HuggingFaceTB/SmolLM2-360M-Instruct |
What is SmolLM2-360M-Instruct-GGUF?
SmolLM2-360M-Instruct-GGUF is a converted version of the SmolLM2 instruction-tuned language model, specifically optimized for deployment using llama.cpp. This model represents a lightweight yet capable option for those seeking efficient language model deployment, featuring Q8 quantization for improved performance while maintaining quality.
Implementation Details
The model is implemented using the Transformer architecture and has been converted to the GGUF format using llama.cpp. It features 362M parameters, making it significantly smaller than many contemporary language models while still maintaining useful capabilities.
- GGUF format optimization for llama.cpp deployment
- Q8_0 quantization for efficient inference
- Supports both CLI and server deployment options
- Compatible with standard llama.cpp installation methods
Core Capabilities
- Instruction-following and conversational tasks
- Efficient local deployment through llama.cpp
- Supports context window of 2048 tokens
- Optimized for resource-efficient inference
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient size-to-performance ratio, being particularly suitable for users who need a lightweight model that can run on consumer hardware while still providing good language understanding capabilities.
Q: What are the recommended use cases?
The model is ideal for conversational AI applications, instruction-following tasks, and scenarios where deployment efficiency is crucial. It's particularly well-suited for local deployment using llama.cpp, making it perfect for developers who need to run AI models with limited computational resources.
