Vikhr-Qwen-2.5-1.5B-Instruct-MLX_8bit
| Property | Value |
|---|---|
| Parameter Count | 434M |
| Model Type | Instruction-tuned Language Model |
| License | Apache 2.0 |
| Supported Languages | English, Russian |
| Format | 8-bit MLX |
What is Vikhr-Qwen-2.5-1.5B-Instruct-MLX_8bit?
This model is an 8-bit quantized version of the Vikhr-Qwen-2.5-1.5B-Instruct model, specifically optimized for the MLX framework. It represents a significant advancement in efficient AI deployment, offering a balance between performance and resource utilization through careful quantization.
Implementation Details
Built using mlx-lm version 0.20.1, this model leverages the Transformers architecture and is packaged in the MLX format for optimized inference. It employs 8-bit precision to reduce memory footprint while maintaining performance.
- Utilizes the mlx-lm framework for efficient inference
- Implements chat template functionality for structured conversations
- Supports both tokenization and generation capabilities
- Optimized for bilingual deployment (English/Russian)
Core Capabilities
- Bilingual text generation in English and Russian
- Instruction-following and conversational abilities
- Efficient inference with 8-bit quantization
- Seamless integration with MLX framework
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized 8-bit implementation in the MLX framework, making it particularly efficient for deployment while maintaining bilingual capabilities in English and Russian.
Q: What are the recommended use cases?
The model is well-suited for text generation tasks, conversational applications, and instruction-following scenarios where resource efficiency is important, particularly in bilingual English-Russian contexts.
