Qwen2.5-14B-Instruct-4bit
| Property | Value |
|---|---|
| Model Size | 14B parameters |
| Format | MLX (Apple Silicon optimized) |
| Quantization | 4-bit |
| Source Model | Qwen/Qwen2.5-14B-Instruct |
| Repository | HuggingFace |
What is Qwen2.5-14B-Instruct-4bit?
Qwen2.5-14B-Instruct-4bit is a specialized version of the Qwen2.5-14B-Instruct model that has been converted to MLX format specifically for Apple Silicon processors. The model features 4-bit quantization, significantly reducing its memory footprint while maintaining performance.
Implementation Details
The model was converted using mlx-lm version 0.18.1, making it compatible with Apple's MLX framework. It can be easily implemented using the mlx-lm Python package, requiring minimal setup and integration effort.
- Optimized for Apple Silicon architecture
- 4-bit quantization for efficient memory usage
- Compatible with mlx-lm framework
- Simple implementation through Python API
Core Capabilities
- Text generation and completion tasks
- Instruction-following capabilities
- Efficient inference on Apple Silicon devices
- Reduced memory footprint through quantization
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its optimization for Apple Silicon through the MLX framework and 4-bit quantization, making it particularly efficient for Mac users while maintaining the capabilities of the original Qwen2.5-14B-Instruct model.
Q: What are the recommended use cases?
The model is ideal for applications running on Apple Silicon devices that require efficient, large language model capabilities, particularly for instruction-following and text generation tasks where memory efficiency is crucial.
