Open4bits / DeepSeek-R1-MLX-2Bit
This repository provides the DeepSeek-R1 model quantized to 2-bit in MLX format, published by Open4bits to enable highly efficient local inference with minimal memory usage and broad hardware compatibility.
The underlying DeepSeek-R1 model and architecture are developed and owned by DeepSeek AI. This repository contains only a 2-bit quantized MLX conversion of the original model weights.
The model is designed for lightweight, high-performance text generation and instruction-following tasks, making it well suited for resource-constrained and local deployments.
Model Overview
DeepSeek-R1 is a transformer-based large language model developed for strong general language understanding and generation.
This release provides a 2-bit quantized checkpoint in MLX format, enabling efficient inference on CPUs and supported accelerators with reduced memory footprint.
Open4bits has started supporting MLX models to broaden compatibility with emerging quantization formats and efficient runtimes.
Model Details
- Base Model: DeepSeek-R1
- Quantization: 2-bit
- Format: MLX
- Task: Text generation, instruction following
- Weight tying: Preserved
- Compatibility: MLX-enabled inference engines and efficient runtimes
This quantized release is designed to balance strong generation performance with low resource requirements.
Intended Use
This model is intended for:
- Local text generation and conversational applications
- CPU-based or low-resource deployments
- Research, prototyping, and experimentation
- Self-hosted or offline AI systems
Limitations
- Reduced performance compared to full-precision variants
- Output quality depends on prompt design and inference settings
- Not specifically tuned for highly specialized or domain-specific tasks
License
This model follows the MIT as defined by the base model creators.
Users must comply with the licensing conditions of the base DeepSeek-R1 model.
Support
If you find this model useful, please consider supporting the project.
Your support helps Open4bits continue releasing and maintaining high-quality, efficient open models for the community.
