SmolLM2-135M-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 135M |
| Format | GGUF |
| Author | MaziyarPanahi |
| Base Model | HuggingFaceTB/SmolLM2-135M-Instruct |
| Downloads | 763,351 |
What is SmolLM2-135M-Instruct-GGUF?
SmolLM2-135M-Instruct-GGUF is a quantized version of the SmolLM2 language model, specifically optimized for efficient deployment using the GGUF format. This model represents a significant advancement in compact language models, offering various quantization options ranging from 2-bit to 8-bit precision to accommodate different performance and resource requirements.
Implementation Details
The model is implemented using the GGUF format, which is the successor to GGML and is officially supported by llama.cpp. It features multiple quantization levels, making it highly versatile for different deployment scenarios.
- Supports multiple quantization options (2-bit to 8-bit precision)
- Optimized for the GGUF format
- Compatible with major GGUF-supporting platforms and libraries
- Designed for text generation and conversational tasks
Core Capabilities
- Text generation and instruction following
- Efficient memory usage through various quantization options
- Compatible with popular frameworks like llama.cpp, LM Studio, and text-generation-webui
- Optimized for both CPU and GPU acceleration
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient size-to-performance ratio, offering a 135M parameter model with various quantization options, making it highly versatile for different deployment scenarios while maintaining reasonable performance.
Q: What are the recommended use cases?
The model is well-suited for text generation and conversational tasks, particularly in scenarios where resource efficiency is crucial. It's ideal for edge devices, quick prototyping, and applications requiring a smaller memory footprint.
