| | --- |
| | license: mit |
| | library_name: transformers |
| | tags: |
| | - mlx |
| | - open4bits |
| | base_model: deepseek-ai/DeepSeek-R1 |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # 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. |
| |
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| | 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. |
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| | The model is designed for lightweight, high-performance text generation and instruction-following tasks, making it well suited for resource-constrained and local deployments. |
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| | --- |
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| | ## Model Overview |
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|
| | 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. |
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| | Open4bits has started supporting **MLX models** to broaden compatibility with emerging quantization formats and efficient runtimes. |
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| | --- |
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| | ## Model Details |
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|
| | * **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 |
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|
| | This quantized release is designed to balance strong generation performance with low resource requirements. |
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| | --- |
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| | ## Intended Use |
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| | This model is intended for: |
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| | * Local text generation and conversational applications |
| | * CPU-based or low-resource deployments |
| | * Research, prototyping, and experimentation |
| | * Self-hosted or offline AI systems |
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| | --- |
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| | ## Limitations |
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| | * 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 |
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| | --- |
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| | ## 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. |
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| | --- |
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| | ## Support |
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|
| | 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. |
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