DeepScaleR-1.5B-6.5bit
| Property | Value |
|---|---|
| Model Size | 1.5B parameters |
| Quantization | 6.5-bit |
| Framework | MLX |
| Source | Converted from agentica-org/DeepScaleR-1.5B-Preview |
| Hugging Face | Link |
What is DeepScaleR-1.5B-6.5bit?
DeepScaleR-1.5B-6.5bit is a specialized language model designed specifically for speculative decoding applications. It's a converted version of the DeepScaleR-1.5B-Preview model, optimized for the MLX framework and quantized to 6.5-bit precision to balance performance and resource efficiency.
Implementation Details
The model is implemented using the MLX framework, requiring the mlx-lm package (version 0.21.4 or later) for deployment. It features a unique architecture optimized for draft model applications in speculative decoding scenarios.
- Optimized for MLX framework implementation
- 6.5-bit quantization for efficient resource usage
- Compatible with chat templates and generation workflows
- Designed for integration with larger models in speculative decoding pipelines
Core Capabilities
- Functions as an efficient draft model for speculative decoding
- Achieves 30% faster TPS for math/code prompts when paired with larger models
- Supports both standard text generation and chat-based interactions
- Optimized performance with LMstudio 3.10 beta
Frequently Asked Questions
Q: What makes this model unique?
DeepScaleR-1.5B-6.5bit stands out for its specific optimization as a draft model for speculative decoding, offering significant performance improvements when paired with larger models like FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-4.5bit.
Q: What are the recommended use cases?
The model is particularly effective when used as a draft model in speculative decoding setups, especially for math and code-related tasks. It's designed to work optimally with LMstudio 3.10 beta and can provide up to 30% faster TPS in these scenarios.
