QVikhr-2.5-1.5B-Instruct-SMPO_MLX-4bit
| Property | Value |
|---|---|
| Model Size | 1.5B parameters |
| Quantization | 4-bit |
| Framework | MLX |
| Author | Vikhrmodels |
| Model Hub | Hugging Face |
What is QVikhr-2.5-1.5B-Instruct-SMPO_MLX-4bit?
QVikhr-2.5-1.5B-Instruct-SMPO_MLX-4bit is a specialized language model optimized for Apple Silicon through MLX framework implementation. It represents a 4-bit quantized version of the original QVikhr-2.5-1.5B-Instruct-SMPO model, specifically converted for efficient deployment on Apple's ML-focused hardware.
Implementation Details
The model utilizes MLX-LM version 0.21.1 for deployment and can be easily integrated using Python. It features built-in chat templating capabilities and streamlined generation functions.
- 4-bit quantization for reduced memory footprint
- Native MLX framework support
- Integrated chat template system
- Optimized for Apple Silicon processors
Core Capabilities
- Efficient inference on Apple hardware
- Chat-based interaction support
- Memory-efficient operation through quantization
- Simple integration through mlx-lm package
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its optimization for Apple Silicon through MLX framework and 4-bit quantization, enabling efficient operation while maintaining performance.
Q: What are the recommended use cases?
The model is particularly suited for applications running on Apple Silicon hardware requiring efficient inference, especially in scenarios where memory optimization is crucial while maintaining instruction-following capabilities.
