SmolLM-1.7B-Instruct-v0.2-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.71B parameters |
| Model Type | Instruction-tuned Language Model |
| Format | GGUF (Multiple quantization options) |
| Author | MaziyarPanahi (Quantized version) |
| Base Model | HuggingFaceTB/SmolLM-1.7B-Instruct-v0.2 |
What is SmolLM-1.7B-Instruct-v0.2-GGUF?
SmolLM-1.7B-Instruct-v0.2-GGUF is a quantized version of the original SmolLM model, specifically optimized for efficient deployment using the GGUF format. This model represents a balanced approach between model size and performance, offering various quantization options from 2-bit to 8-bit precision to suit different deployment scenarios.
Implementation Details
The model is implemented in the GGUF format, which is the successor to GGML, introduced by the llama.cpp team. It supports multiple quantization levels (2-bit through 8-bit precision) to optimize the trade-off between model size and performance for different use cases.
- Multiple quantization options (2-bit to 8-bit)
- GGUF format optimization for efficient deployment
- Compatible with various inference frameworks
- Designed for conversational and instruction-following tasks
Core Capabilities
- Text generation and completion
- Instruction following
- Conversational AI applications
- Efficient local deployment through various clients
- Cross-platform compatibility
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient implementation in GGUF format with multiple quantization options, making it highly versatile for different deployment scenarios while maintaining a relatively small parameter count of 1.7B.
Q: What are the recommended use cases?
The model is well-suited for local deployment in applications requiring text generation and instruction following, particularly where resource efficiency is important. It can be used with various clients including LM Studio, text-generation-webui, KoboldCpp, and GPT4All.
